A computer validated Portuguese to English transformational grammar [Reprint 2017. ed.] 9783111358758, 3111358755

Table of contents :
Frontmatter --
TABLE OF CONTENTS --
I. INTRODUCTION --
II. PHRASE STRUCTURES --
III. ONE-STRING GRAMMATICAL TRANSFORMATIONS --
IV. TWO-STRING GRAMMATICAL TRANSFORMATIONS --
V. COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR --
V. COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR --
APPENDIX: SYMBOLS USED IN THIS GRAMMAR --
BIBLIOGRAPHY --
INDEX

Citation preview

JANUA LINGUARUM STUDIA

MEMORIAE

N I C O L A I VAN WIJK

DEDICATA

edenda curat C. H. V A N

SCHOONEVELD

Indiana University

Series

Practica,

136

A COMPUTER VALIDATED PORTUGUESE TO ENGLISH TRANSFORMATIONAL GRAMMAR

by

JAMES L A R K I N WYATT

IS 1972

MOUTON THE HAGUE • PARIS

© Copyright 1972 in The Netherlands. Mouton & Co. N . V . , Publishers, The Hague. No part of this book may be translated or reproduced in any microfilm, or any other means, without written permission

form, by print, photoprint, from the publishers.

L I B R A R Y OF CONGRESS CATALOG CARD N U M B E R :

Printed in Hungary

79-189715

TABLE OF CONTENTS

I. Introduction

7

II. Phrase structures

28

III. One-String Grammatical Transformations 3.1 3.2 3.3. 3.4.

61

Passive Compound-Alternate Negative Interrogative

61 62 62 66

IV. Two-String Grammatical Transformations 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. 4.7. 4.8. 4.9. 4.10. 4.11. 4.12. 4.13. 4.14.

Modification Sentence as Subject, Complement, or Direct Object Interrogative as Subject or Direct Object Sentence as Time Element Sentence as Location Element Sentence as Manner Element Optional Unknown Relative Transformations Subordination Coordination Reduced Sentences Comparison of Equality and Inequality Superlative Comparison Portuguese Obligatory Transformation Portuguese Optional Transformation

V. Computer Output from Programmed Grammar

72 . . .

72 76 80 82 83 83 83 84 87 91 92 95 96 96 97

Appendix: Symbols Used in this Grammar

431

Bibliography

434

Indexe

438

I INTRODUCTION

1.1. THE SCOPE OF THIS WORK

This introductory chapter describes and discusses a Portuguese to English generative-transformational grammar which has been programmed for automatic computer production of kernel sentences and transformations of those kernels. A representative sample of randomly generated computer output accompanies this bilingual grammar of syntax. The paired Portuguese and English strings are translations of each other. This grammar is Portuguese-oriented in that the formulation of phrase structure and transformation rules was based on the syntactic structures identified first in a corpus of spoken Brazilian Portuguese. Only after Portuguese structures were identified were corresponding English structures matched with them. The format of this grammar follows to some degree that of the earlier transformational grammars. Following the phrase structure rules, which include several minor optional transformations and some concord rules, are optional one-string transformations formulated so that one may be applied after the other, making use of a kernel as the input for the first transformation and thereafter using as input either a kernel or a transformed kernel resulting from the previous transformation. Although the computer output shows each one-string transformation applied after the other and operating on the same kernel, any one-string transformation could have been skipped, or a new kernel could have been used as input. Optional two-string transformations are formulated for application to an untransformed kernel or to a kernel having been subjected only to a one-string transformation. No provision has been made for input the result of a two-string transformation; hence, there is no ordering of the two-string transformations, as is the case for one-string transformations. The computer program which automated this grammar and provided the randomly generated output was written for IBM computers of the "70" series (i.e., 704, 7040, 709, 7090, 7094). The program is not included here

8

INTRODUCTION

because of the bulk it would add, and because the computers on which it would run have largely been replaced by newer models. Automation was an important feature in the preparation of this grammar because computer output was used as feedback during the formulation of phrase structure and transformational rules. I t is in this sense t h a t the term 'validated' is used in the title of this work. In no sense is the claim made t h a t the formulations contained in this grammar are in themselves valid and t h a t the computer output represents valid sentences in Portuguese and English. The computer output will, however, enable the reader to arrive at his own conclusions as to the validity of the Portuguese and English sentences, the result of the intent of the grammar. The reader may also judge the adequacy of the grammar by an examination of the computer output. All major features of the grammar were programmed, but a number of optional transformations were omitted. Since the output was generated randomly, not all features which were programmed appeared in any single run of the program. The sample of output included here is typical of a great many executions of the program.

1.2. THE METHOD OF PROCEDURE

The corpus of spoken Brazilian Portuguese used as the basis of this grammar was obtained through approximately 150 hours of contact with Mr. Ubiratâo Pimentel Sayd, who had lived in Sâo Paulo all his life before coming to the United States. After graduating from secondary school, Mr. Sayd worked in his father's drugstore and as a bank teller. He came to the United States for the purpose of learning English and earning a university degree in engineering. Elicitation for this work began only a few days after Mr. Sayd arrived from Brazil. At t h a t time he spoke only a few words of English, and those with considerable hesitancy. Mr. Sayd seemed to have practically no formal knowledge of Portuguese grammar, and he was not intimately familiar with any language other than Portuguese. During the period of elicitation Mr. Sayd lived in a married sister's home where Portuguese was used for communication. Since the writer had already assembled data from two other native Brazilians and from recordings of unrehearsed Brazilian Portuguese in the form of monologues, certain shortcuts were taken in arriving at details of analysis. No old data were used, however, without further validation by Mr. Sayd. The writer served as his own informant for English, making use of his intuitive knowledge of his native tongue. When sufficient results were validated by the informant, work on the corresponding English structures was begun, and when sections of correlated Portu-

INTRODUCTION

9

guese and English structures were tentatively formulated, computer programming was begun. At times new data were being gathered while preliminary analyses were being programmed and computer output was being used to validate earlier analyses. The noun phrase and verb phrase were programmed separately at the beginning, and many, many iterations of those programs were executed for testing purposes. The randomly generated output used as feedback proved useful in discovering oversights and bringing to light other deficiencies. When these programs were running satisfactorily, they were joined, making possible the production of subjects and predicates, and to them were added routines to make possible transitive, equational, and intrasitive sentences. Other routines were added for time, place, and manner expressions. While the composite program for generating kernel sentences was being tested, programming of the transformational parts was begun. An effort was made to include in this grammar an accounting for all of the Portuguese structures actually encountered in the corpus or suggested by the data. As stated above, this grammar is Portuguese-oriented. English structures were self-elicited only after Portuguese structures were identified. Another way to set about the task of devising a bilingual grammar could have been to make separate and independent analyses of the two languages and then to have sought to equate the parts of the one with those of the other. In this case, however, the writer's interest was to identify and formulate Portuguese syntactic structures in the data and then provide a means for expressing those structures and their semantic content in English. In practically all cases a broad parallelism in syntactic structures was possible. In some instances the selection of lexical items influences the degree of parallelism, as in the case of adjectives which can take adverbializing suffixes denoting manner. Here the lexical lists of the two languages do not match perfectly. An adjective plus an adverbializing suffix in one language might be equated semantically with a preposition plus a noun in the other (i.e., Portuguese dificil plus -mente vs. English with difficulty, not *difficultly; English calm plus -ly vs. Portuguese com calma, not *calmamente). The effort in this grammar has been to show where syntactic-semantic parallelism can exist, not to point out the instances where there is a lack of such parallelism. The lexical items appearing in the grammar, therefore, favor syntacticsemantic parallelism. No Portuguese or English structures were excluded, however, to maintain the sought parallelism.

10

INTRODUCTION

1.3. SOME DETAILS OF THIS GRAMMAR

In general, this grammar follows the notational system described by Emmon Bach in An Introduction to Transformational Grammars.1 Several minor variations are exhibited here, however. The primary variation is t h a t right-hand parentheses, braces, and brackets are omitted when not followed by any other symbols. An argument for doing so could be the very one Bach offers for not using a concatenation symbol between symbols separated by parentheses, braces, or brackets: the latter automatically denote separation, and any additional symbols for t h a t purpose amount to redundancy. 2 Similarly, if no symbols would follow closing parentheses, braces, or brackets, their use would be superfluous. Brackets enclosing a group of concatenated symbols on a single line are used here to show the boundaries of the groups of concatenated symbols, as the parentheses are used in algebraic formulas. The hyphen is used here for two purposes: (1) in the development of the symbol Aux to show that any symbol or symbols between it and a concatenation symbol or a closing parenthesis, brace, or bracket will follow in the string the next occurring symbol when its selection is effected, and (2) in terminal symbols to designate bound morphemes. The hyphen is used to make explicit the implicit discontinuity in Noam Chomsky's Aux rewrite rule. 3 Explicitness is necessary to this bilingual 'common core' type grammar because Portuguese and English do not operate alike, namely, in the simple future tense and in the case of marked infinitives. When alternative replacements for a symbol are indicated on a single line to effect space-saving, braces are not used to enclose the group of alternative replacements. Commas alone indicate an obligatory selection from the alternatives. Spaced periods indicate incomplete listings. Following the more or less standard transformational notation, then, parentheses enclose optional items; braces indicate an obligatory single selection from the vertical list of enclosed alternatives, and one set of brackets relates an obligatory selection from its vertical list with an obligatory selection from another set or other sets of brackets, line by line. The explicit concatenation symbol is the plus sign. Arrows, single or double, separate the input of a rule from its output. Terminal symbols are underlined for the purpose of distinguishing them from abstract symbols. 1

Emmon Bach, An Introduction to Transformational Grammars (New York: Holt, Rinehart and Winston, Inc., 1964), pp. 17 — 26. 2 Bach, p. 19. 3 Noam ChomBky, Syntactic Structures ('s-Gravenhage: Mouton and Co. 1957), p. 39.

INTRODUCTION

11

Obligatory transformations are interspersed in the phrase structure rules, the optional one-string transformations, and the optional two-string transformations where they apply. In the main, this is a forward-moving grammar avoiding as much backtracking as possible. That means t h a t an effort has been made to postpone the rewriting of certain symbols until other rules concerned with t h a t rewriting have been stated. However, to avoid large amounts of intervening material not affected by the rewriting of a particular symbol, some backtracking has been introduced intentionally. This is especially true in the case of the symbol for the noun phrase. Notes appear on occasion in this grammar reminding t h a t an earlier rule must be applied. This applies especially to pronouns concerning person, number, and gender. Obviously it is not possible to devise a grammar which is entirely forwardmoving, because occasions would arise necessitating the simultaneous appearance of several rules. Lacking an entirely forward-moving grammar, the reader is spared a certain amount of repeated scanning of the rules by the reminders in the form of notes. In the transformational sections structural descriptions follow a colon after an abstract symbol. Certain symbols were used in the phrase structure section to facilitate more explicit structural descriptions (such as Subj, DO, 10, Ag, etc.). For no special purpose except for mnemonics, a single arrow in a rewrite rule signifies one string of input, and a double arrow signifies two strings of input. The writer has made an effort to choose arbitrary symbols of mnemonic value. At a sacrifice of what some might call simplicity, symbols are often longer than they need to be for any mechanical application. Traditional grammatical terminology is used to a great extent, although those terms have meaning only in the context of the rules in which they appear. Out of that context, terms of all kinds are simply convenient labels. A list of symbols used in this grammar appears in the Appendix, where rule numbers are given for the first occurrence of several principal symbols. Also, menmonics are given for symbols where applicable.

1.4. LIMITATIONS OF THIS GRAMMAR

This grammar produces, or should produce, grammatical sentences in pairs, one sentence in Portuguese and one sentence in English, the one being the equivalent of the other in semantic content, except where one-to-many or many-to-one semantic relationships are inherent to the structures of the languages. Leaving aside for the moment a discussion of what is meant by GRAMMATICAL and SEMANTIC equivalent, the claim is made that within its scope

12

INTRODUCTI

this grammar produces a great many of the possible syntactic structures of each language, more of those possible in Portuguese than in English. I t is hoped, but certainly not claimed, t h a t few errors exist in this grammar. While the computer output in both languages appears odd, this is due primarily to lexical selection and to the frequent appearance of low occurrence grammatical possibilities dependent on certain very special contexts. The term GRAMMATICAL is used here to describe those sentences which a native speaker might accept as possible, however odd they might sound or appear. Odd sentences include those which in terms of the real world are false, improbable, impossible, or absurd. I n short, a grammatical sentence is one which displays a generally accepted syntactic pattern and also displays formal grammatical agreement. Real world semantic content may or may not be present, depending on whether the lexical items are known to the one inspecting the sentence. A string having semantic meaning only (i.e., without a generally accepted syntactic pattern and/or without formal grammatical agreement) is not a grammatical sentence according to the criteria set forth here. Sentences which are false, improbable, impossible, or absurd in meaning are defined as grammatical in an attempt to bring about convergence of native speakers' judgment, which was demonstrated to vary substantially by Archibald A. Hill's testing of Chomsky's examples of degrees of grammatically. 4 Hill's notions of grammaticality are necessary to defend the output of this grammar. Since there can be no context within which the computer-generated sentences (actually pseudo sentences) can appear, the question which will have to be asked of a questionable sentence is whether a native speaker might accept the sentence with the understanding that it was odd b u t nevertheless could be spoken within some special context or represent grammatical nonsense. The idea of a sliding scale of grammaticality, as suggested by Ralph M. Goodman and Robert P. Stockwell, is not recognized here. 5 Those degrees are excluded here by the inclusion of odd sentences with those of unquestioned grammaticality. Those authors consider GRAMMATICAL and COMPATIBLE as two points on a sliding scale.6 Here there is recognized something perhaps akin to Charles F. Hockett's idea of a "pattern of expectation" on the part of an inspector of these computer-generated sentences.' The odd sentence is the result of an unknown or unexpected word or combination of words in a "pattern of expectation". The inspector of the sentence is called upon to •

Archibald A. Hill, "Grammaticality", Word, Vol. 17, No. 1 (April, 1961), pp. 1 — 10. Ralph M. Goodman and Robert P. Stockwell, "The Degrees of Grammaticalness"; an undated, privately circulated paper. 6 Goodman and Stockwell, p. 4. 7 Charles F. Hockett, "Grammar for the Hearer", Symposia in Applied Mathematics, Proceedings, Vol. X I I (Providence: American Mathematical Society, 1961), pp. 220—36. 5

INTRODUCTION

13

pretend that he is the speaker and to search for explanations justifying the odd sentence. If the inspector is successful as the pretended speaker, the odd sentence is judged grammatical. If the inspector is unsuccessful after displaying great skill and imagination in an attempt to explain the sentence, it is not grammatical. In a sense, a grammar of the type presented here might be thought of as a grammar for both the hearer and the speaker. I t would be better, perhaps, to say t h a t this might be thought of as a grammar BY the hearer and BY the speaker, because it could be assumed t h a t when a hearer judges utterances, he applies HIS rules of grammar, and so in the case of the speaker, who applies HIS rules in the production of sentences. Rather than consider that the hearer crosses the line of participation, Hockett suggest t h a t "one may at moments function purely as a hearer. The alternative to this is not to function purely as a speaker, but to function BOTH as speaker and hearer." 8 Hockett's concept of the dual function of the speaker, except in reverse (i.e., the hearer not to function purely as as a hearer, but to function BOTH as a hearer and speaker), seems useful in getting at odd sentences to make them fall within the fold of grammatical sentences. I t must be assumed t h a t the hearer, the inspector of the sentences of this grammar, is expert, sympathetic, and willing to serve also as speaker. He is not the casual, man-on-thestreet informant Chomsky would seem to trust to spur of the moment judgment. 9 I n spite of the fact that research lends support to "Chomsky's contention that non-occurring and meaningless sequences can be grammatical", 1 0 t h a t same research has suggested t h a t " . . . as a general proposition . . . the linguistic definition of any term will never correspond perfectly with its behavioral definition, however obtained." 1 1 The proposition here is intended to bring about some little degree of correspondence between linguistic and behavioral definitions. The kind of sentence claimed not to be produced by this grammar, tnen, would be the kind to which Roman Jakobson's " t r u t h test" could not even be applied, thoroughly degrammaticalized utterances representing pure nonsense. 12 A stricter view of grammaticality could be taken, but for the purpose of this grammar the writer is forced to propose this looser view. Goodman and 8

Hockett, p. 235. Chomsky, op. cit., p. 11. 10 Howard Maclay and Mary D. Sleator, "Responses to Language Judgments of Grammatioalness", IJAL, Vol. X X V I , No. 4 (October, 1960), p. 279. 11 Maclay and Sleator, p. 280. 12 Roman Jakobson, "Boas' View of Grammatical Meaning", The Anthropology of Franz Boas: Essays on the Centennial of his Birth, ed. Walter Rochs Goldschmidt ([Menasba, Wisconsin]: American Anthropological Association, [1959]), p. 144. 9

14

INTRODUCTION

Stockwell incorporated morphemic compatibility into their scale of grammatic a l l y , 1 3 but this writer's loose view of grammaticality does not permit considerations of compatibility. Although this grammar does not contain lexical entries stating semantic and other features, as has been proposed, 14 a minimal effort was made in the computer program to make the pseudo sentences in the output resemble genuine sentences. As one can judge from a glance at the computer output, the formulation of semantic rules for even a tiny fragment of the lexical stock would be a prodigious undertaking. The use of the computer here underscores the effort t h a t task would entail. Perhaps the only practical means to test semantic rules would be via computer. Victor H. Yngve was perhaps the first to stress the usefulness of the computer in investigating semantic relationships. On the occasion of reporting on the random generation of sentences in English by computer, Yngve stated t h a t ". . . some of the original vocabulary words seem to change drastically when they are embedded in different, though similar contexts. We thus appear to have . . . a fruitful method of examining the relation of meaning of words to their context." 1 5 Agreement concerning what is meant by SEMANTIC EQUIVALENT is also needed in order to accept this grammar on its own terms, but, admittedly, nothing approaching a precise statement can be made. Translation is, in a sense, merely incidental to this work. The writer is neither prepared to make any precise statements of his own as to what translation amounts to, nor is he able to cite precise statements of experts in the field. Perhaps it will be useful, if not enlightening, to examine a few statements by those who have a special interest in the field of translation and/or meaning: Taken in its most general sense, translation is the substitution of one language for another to express the same set of ideas. It should proceed by a one-one substitution of symbols for each of the ideas expressed, together with the additional changes required by a possible change in the syntactic rules. 16

13

Goodman and Stockwel, loc. cit. Noam Chomsky, Aspects of the Theory of Syntax (Cambridge: The M.I.T. Press, 1965). 16 Victor H. Yngve, Random Generation of English Sentences, Memo 1961 — 4 (Cambridge: Mechanical Translation Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, January 30, 1961; reprinted December, 1964), p. 11. 16 R. H. Richens and A. D. Booth, "Some Methods of Mechanized Translation", Machine Translation of Languages, ed. William N. Locke and A. Donald Booth (New York: The Technology Press of the Massachusetts Institute of Technology and John Wiley and Sons, Inc., 1955), p. 25. 14

INTRODUCTION

15

Empirical meaning is what remains when, given a discourse together with all stimulatory conditions, we peel away the verbiage. I t is what the sentences of one language and their firm translations in a completely alien language have in common. 17 To translate a senctence from one language to another is somehow to discover its meaning and then to construct a sentence in the new, or target language that possesses the same meaning. 18 Dans l'un des plateaux nous déposons l'un après l'autre les mots de l'Auteur, et dans l'autre nous essayons tour à tour un nombre indéterminé de mots appartenant â la langue dans laquelle nous traduisons cet Auteur, et nous attendrons l'instant où les deux plateaux seront en équilibre. 19 All of the above statements seem to point t o the 'naked idea' theme perhaps best synthesized in the words of Jacques Barzun: The act of translating does not consist in carrying words across a no-man's-land, but in answering the question: "How would I say this if the notion occurred to me for the first time in my own tongue?" Not finding words but turning phrases-hence being sure of what the foreigner thinks and what the native says: two minds with twin thoughts. 20 The latter words describe the approach the writer would consider valid for the translation of discourse, and if useful for discourse, it would appear t h a t the same approach would serve for the translation of single sentences. Such an ideal method of translation cannot be claimed for this work, however. The translation here is n o t linear, but it is effect b y units of syntactic structure, w i t h one or more lexical items in a structure corresponding on a dictionary basis of semantic equivalence with one or more lexical items in the structure of the other language. I n practically every instance the lexical equivalents stand on a one-to-one basis. This has been possible b y the purposeful avoidance of idioms. J o h n Moyne's definition of an idiom is meant here: " . . . a stretch of t w o or more . . . words which is treated as a unit and is translated . . . not b y its individual components but b y a special equivalent structure which reflects the source concept." 2 1 17

Williard V. Quine, "Meaning and Translation", On Translation, ed. Reuben A. Brower (Harvard Studies in Comparative Literature 23) (Cambridge, Massachusetts: Harvard University Press, 1959), p. 148. 18 John Hollander, "Versions, Interpretations, Performances", On Translation, ed. Reuben A. Brower (Harvard Studies in Comparative Literature 23) (Cambridge, Massachusetts: Harvard University Press, 1959), p. 207. 19 Valéry Larbaud, Sous l'invocation de Saint Jérôme (5a édition; [Paris]: Librairie Gallimard, 1946), p. 83. 20 Jacques Barzun, "Food for the N.R.F. or My God! What will you h a v e î " , Partisan Review, Vol. X X , No. 6 (November—December, 1953) pp. 664 — 65. 21 John Moyne, "Idiomatic Structures in Machine Translation", General Analysis Technique, Russian —English Research Reports, Part VII (Georgetown Occasional Papers on Machine Translation No. 8) (Washington, D. C.: Institute of Languages and Linguistics, Machine Translation Research Center, July, 1959), p. 1.

16

INTRODUCTION

The translation of idioms would represent a necessary appendage to a complete grammar, with each idiom being dealt with separately, or at best in small groups. But since this grammar makes no pretense of being complete, idioms are not treated. Some few provisions are made, however, for changing from a particular syntactic pattern in one language to a different syntactic pattern in the other to preserve the semantic equivalence. William E. Bull has criticized textbook writers for classifying all fixed phrases as idioms. He states t h a t such a procedure absolves the writers of explaining those phrases. 22 In this grammar it was necessary on occasion to oppose one 'fixed phrase' to a different syntactic structure in the other language, since there was no alternative, if semantic equivalence was to be maintained. I n essence, then, lexical items are said to be equivalent if one could reasonably expect native speakers of the separate languages to utter those items in reacting to the same stimulus. While the items are simply matched on a one-to-one basis in an ad hoc intuitive manner, Eugene A. Nida's words are well taken: The basic principles of translation mean that no translation in a receptor language can be the exact equivalent of the model in the source language. That is to say, all types of translation involve (1) loss of information, (2) addition of information and/or skewing of information. 23

Even if there were an effort here to deal in any precise way with the common concept as expressed by two languages, it would be difficult to disregard J . R. Firth's admonition: "One must beware of building bridges between two different languages by means of 'naked ideas'." 24 He admits to a need for taking into account the human situation in translating, b u t he argues that "the English word 'kindness' . . . does not represent a 'naked idea' of any value to linguistics, and in t h a t sense no other language in the world has a word for 'kindness'." 25 The writer of this work asserts that the word kindness does not have precisely the same meaning for all speakers of the English language, much less represent a "naked idea" which could be clothed in another language. Firth asks of us:

22

William E. Bull "Spanish Adjective Position", Hispania, Vol. X X X V I I , No. 1 (March, 1954), note 10, p. 37. 23 Eugene A. Nida, "Bible Translating", On Translation, ed. Reuben A. Brower (Harvard Studies in Comparative Literature 23) (Cambridge, Massachusetts: Harvard University Press, 1959), p. 13. 21 J. R. Firth, "Linguistic Analysis and Translation", For Roman Jakobson, comp. Morris Halle et al. (The Hague: Mouton and Co., 1956), p. 134. 25 Firth.

INTRODUCTION

17

Do wo know how we translate ? Do we know what we translate ? If we could answer these questions in technical terms we should be on the way to the formulation of a new and comprehensive general theory of language and firmer foundations in philosophy.20

With its limitations, this grammar is intended to be at least as complete as the average pedagogical foreign language textbooks at the beginner's and intermediate level, except for its compact lexicon, lack of rules for obligatory and optional contractions, idiom and irregular word lists, and certain irregularities. Object pronoun placement in Portuguese has been simplified in t h a t other common placements are not included. One concession has been made to traditional Portuguese textbooks, and t h a t is t h a t the little used familiar second person verb forms and their corresponding subject and other pronouns have been included. The informant did, however, make some use of these forms in certain familiar, stereotyped utterances, if not in ordinary conversation. 1.5. THIS GRAMMAR AS RELATED TO SOME OTHERS OF A TRANSFORMATIONAL, CONTRASTIVE, OR AUTOMATED NATURE

The title and organization of this work immediately suggest an adaptation for bilingual description of Chomsky's earlier model of transformational grammar to provide a bridge from one language to another, and with travel across that bridge in a particular direction. Paul Schachter, in his contrastive analysis of English and Pangasinan, placed two transformational descriptions back to back, in a sense, and then strained the English grammar through the sieve of Pangasinan grammar, leaving as the residue that part of English for which there was no Pangasinan counterpart. 27 The residue was the principal interest of Schachter's study. 28 Schachter's approach coincided with Zellig S. Harris' notion of a transfer grammar, which he saw as a set of instructions leading from one language to another via specification of structural similarities and differences. 29 Harris, in outlining a method for bridging languages, saw one means of going from language A to B as appending to a grammar of language A a grammar of t h a t part of language B not common to language A. 30 Harris also saw as a starting point a grammar Z which would not generate sentences directly in either language A or B but which would serve as an 26

Firth, p. 139. Paul Schachter, "A Contrastive Analysis of English and Pangasinan" (unpublished Ph. D. dissertation, The University of California at Los Angeles, 1959). 28 Schachter, p. 1. 23 Zelling S. Harris, "Transfer Grammar", IJAL, Vol. XX, No. 4 (October, 1954), pp. 259 — 70. 30 Harris, p. 260. 27

18

INTRODUCTION

incomplete skeleton of grammar for both languages, requiring branching in the appropriate direction to add A—Z for sentences in A or B—Z for sentences in language B. 31 It is the latter, or 'common core' approach which is attempted here, with the necessary branching. This is opposed to the backing up of two grammars and straining one through the other, as in the case of Schachter's work. This grammar will produce sentences in Portuguese, in English, or in both languages, while Schachter's grammar was designed as a one-way grammar. Although the title of this work suggests a particular direction of travel across the bridge from one language to another, that direction is merely a reflection of the orientation of this grammar. For each utterance elicited in Portuguese, English possibilities were sought, keeping in mind that a common core would in practically all cases bind the possibilities in one language to those in the other. Separate and independent analyses were not first made and then strained, one through the other, although that procedure might well have merit. Certain disadvantages might accrue, however, in making separate analyses and then attempting to equate them. The common core which evolved in this grammar must contain a certain amount of skewing, perhaps advantageous if practical application of this grammar is effected in the areas of the preparation of pedagogical materials, automatic linguistic analysis, or automatic translation. A number of the formulations in this grammar which apply to English were arrived at quite differently than in Robert B. Lees' transformational grammar treating English.32 It is believed, but perhaps cannot be demonstrated, that this particular bilingual grammar is the better for its inherent skewing brought about by the method of procedure. Schachter's study rewrites the intonational pattern symbol along the lines suggested by Stockwell.33 In this grammar the intonational pattern symbol is generated but never rewritten. Also, Schachter recognized two types of nuclei and rewrote symbols for both. In this grammar two types of nuclei are recognized, but only one of the symbols is rewritten. Representations of the symbol S in the transformational rules stand only for the nucleus symbol rewritten in the phrase structure rules. The lexicons contained in Schachter's work and in this one are, for all practical purposes, equally brief. Schachter made an effort to set up some 31

Harris. Robert B. Lees, The Grammar of English Nominalizations, IJAL, Vol. X X V I , No. 3, Part 2 (Publication 12 of the Indiana University Research Center in Anthropology, Folklore, and Linguistics) (Bloomington, Indiana: Indiana University Research Center in Anthropology, Folklore, and Linguistics, 1960). 33 Robert P. Stockwell, "The Place of Intonation in a Generative Grammar of English", Language, Vol. 36, No. 3 (July-September, 1960), pp. 370—78. 32

INTRODUCTION

19

cooccurrence restrictions, while this grammar does not. Several broad cooccurrence restrictions were imposed in the computer program automating this grammar, however. These restrictions were not imposed for any grammatical interest, but, as stated earlier, merely to lend some air of authenticity to the computer output. Even after having included cooccurrence restrictions in the Pangasinan to English study, Schachter said t h a t his "cross-classification of E. [English] nouns is far from satisfactory." 3 4 The writer feels that attempts at cooccurrence restrictions in a grammar of this type are not only unfruitful but also not especially relevant. Harris' words would seem to support the first part of this statement, at least: To describe a language in terms of the co-occurrence of the individual morphemes is virtually impossible; . . . it is in general impossible to obtain a complete list of co-occurrents for any morphene; and in many cases a speaker is uncertain whether or not he would include some given morphene as a co-occurrent of some other. 35

Concerning transformations, Schachter's work treats several types this grammar does not, and this grammar treats several types Schachter's work does not. The number of transformations in each case is comparable. Another transformational grammar contrasting the syntax of two languages is t h a t of Chalao Chaiyaratana concerned with English and Thai, resembling Schachter's work in that separate descriptions of syntax are presented, and they are followed by a tabular comparison of the structures. 36 In that study phonology and morphology are treated only incidentally, as is the case in the grammar at hand, where the symbol I P is the only reference to phonology-

A monolingual generative, but not transformational, grammar of French has been prepared by David Allen Dinneen. 37 That grammar, which is programmed for a computer, is cast in the mold of a modified finite state model. I t utilizes a "grammar of specifiers" which selects sentence types at the beginning of generation rather than produce a kernel and then resort to transformations. 38 Dinneen's print-out included a structural description of each sentence. While this print-out included here as Chapter V does not show a structural

34

Schachter, op. cit.-, p. 117. Zellig S. Harris, "Co-occurrence and Transformation in Linguistic Structure", Language, Vol. 33, No. 3 (July—September, 1957), p. 285. 38 Chalao Chaiyaratana, "A Comparative Study of English and Thai Syntax", (unpublished Ph. D. dissertation, Indiana University, 1961). 37 David Allen Dinneen, A Left-to-Right Generative Grammar of French (Cambridge, Massachusetts: Mechanical Translation Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, 1962). 38 Dinneen, pp. 39—46. 35

20

INTRODUCTION

description, one did exist in the computer core storage and could, with only minor changes in the output routine, have been included in the print-out along with each sentence. Arnold Chase Satterthwait has joined with a computer routine for randomly generating sentences in Arabic other routines which in turn parse the Arabic sentences and then render English translations. 39 Following Yngve's framework for syntactic translation, 40 Satterthwait went from Arabic to English by means of structural equivalence rules. His Arabic sentence grammar, based on a small and limited corpus, generated transitive sentences of the 'kernel' type. The conspicuous feature of Satterthwait's work not included in the computer program automating this grammar is the parsing routine. Perhaps it would be of some interest to make a gross comparison between the English part of this grammar and Lees' grammar. There is great similarity in the phrase structure parts, considering the common core of this grammar and the English branching versus Lees' single English component. The most noticeable differences are Lees' effort at cooccurrence restrictions and this grammar's development in detail of the determiner phrase as opposed to Lees' single unrewritten symbol representing unenumerated ai tides and demonstratives. 41 The determiner phrase in this grammar includes more than articles and demonstratives, making certain of Lees' transformations unnecessary in this grammar. For example, Lees has to transform The man first to go to get The first man to go.4'2 Presumably, Lees' grammar would produce The men four, and that would have to be transformed to give The four men. Such is not the case in this grammar. The determiner phrase formulations contained in this grammar would seem to obviate at least some of Hill's criticism of Lees' grammar concerning extra complication brought about by depending on transformations to bring about modifier-noun constructions. 43 Lees' grammar includes several one-string transformations not included

39

Arnold Chase Satterthwait, Parallel Sentence Construction Grammars of Arabic and English (Cambridge, Massachusetts: Mechanical Translation Group, Research Laboratory of Electronics, Massachusetts Institute of Technology 1962), and "Sentence-For-Sentence Translation: An Example", Mechanical Translation, Vol. VIII, No. 2 (February, 1965), pp. 1 4 - 3 8 . 40 Victor H. Yngve, "A Framework for Syntactic Translation", Mechanical Translation, Vol. IV, No. 3 (December, 1957), pp. 59 — 65. 41 Lees, op. cit., pp. 14—15. 42 Lees, p. 98. 13 Archibald A. Hill, Review of The Grammar of English Nominalizations, by Robert B. Lees, Language, Vol. 38, No. 4 (October—December, 1962), p. 442.

INTRODUCTION

21

here: "ellipsis", 44 "contraction", 4 5 "so-too", 46 "neither-nor", 47 and "either". 48 Contraction was not included in this grammar because it is not essential for going to English from Portuguese, representing an extra, optional feature in English. Contraction would certainly be included in a more sophisticated version of this grammar. Lees' "ellipsis" transformation, producing He did, Can he and Who always must, seems to treat a fragmentary type of sentence peculiar to dialogue and hence not normal in the sense t h a t other sentences are. These same constructions could be derived from this grammar's Porutuguese and English sentences, and they would be included were this grammar's aims only slightly higher. Lees' other one-string transformations are considered in this grammar to be derived from two-string optional transformations. Lees' "so-too" examples, So can he and He can too are considered fragments of some such sentences as Mary can run and so can he and Mary can run and he can too. The same is true in the case of Lees' "neither-nor" and "either" transformations. In this grammar they would result from two-string optional transformations, being only fragments of Mary cannot play and neither can he, Neither can Mary play nor can he, and Mary cannot play and he cannot either. Since Lees' principal interest is nominalizations, it is clear why this grammar may contain certain two-string transformations not found in The Grammar of English Nominalizations. Of course, Lees' grammar covers many points not treated here, but it would not be difficult to develop at least some of Lees' English nominals from paraphrases of some of his source sentences. Lees' "action nominal" in His drawing fascinated me(because he always did it lefthanded) could be derived from I was fascinated when he drew (because he always did it left-handed) 49 His "gerundive nominal" in His eating vegetables (is surprising)50 and his "infinitival nominal" in For him togothere(is strange)51 could be considered further possibilities stemming from the English factive, since there are no counterparts in Portuguese for these nominals and the factive seems about the only possible point of departure to develop them in English. Lees' "infinitive nominal" in I can't wait for him to go could be derived from 1 can't wait until he goes,5i and his "infinitival nominal" in The new car is for him to drive could be derived from The new car is [exists] in order that he drive it.53 44 45 46 47 48 48 50 51 52 63

Lees, Lees, Lees, Lees, Lees. Lees, Lees, Lees, Lees. Lees.

op. cit., p. 40. p. 41. pp. 41 — 42. p. 42. p. 64. p. 71. p. 73.

22

INTRODUCTION

The fact t h a t all of the possibilities which exist in English do not exist in Portuguese would force one to derive the additional features in English from some other feature held in common by Portuguese and English. Some of the formal features offered by Lees either do not seem to hold in the writer's dialect or seem to be due to the results of very special cooccurrence restrictions in the English language. Some might wonder why there are two transformations in this grammar providing for recursiveness, a one-string optional transformation labeled "compound-alternate" and a two-string transformation labeled "coordination of sentences". Although there are two rules here where only one could do the job (i.e., the two-string transformation), this seems to simplify the grammar, rather than complicate it, at least as far as automation is concerned. In order to arrive at John and Mary ivill leave the city tomorrow by train with great remorse by way of joining separate sentences, considerable testing would have to be done to determine whether all elements in both sentences after the subject were syntactically and semantic-ally the same. This equivalence can be stated rather easily in formal language, but it is somewhat complicated to establish the equivalence within the computer, at least more complicated than making use of a recursive rule for the noun phrase. One slight advantage of a recursive rule for generating syntactic elements for a single string is t h a t deletions are avoided, amounting to a measure of simplicity to offset the disadvantage of an extra rule.

1.6. AUTOMATION AS A TOOL FOR T H E TESTING A N D EVALUATION OF GRAMMARS

Although the application of a computer to generate strings in Portuguese and English may be novel, the idea of making use of a computer to test the results of linguistic research is not. While the first thoughts of using the computer in the area of linguistics were exclusively concerned with machine translation, 54 it was not long before other uses were seen. In early 1961, Yngve devised a computer program automating a grammatical sketch of English based on the first ten sentences of a children's book. 55 The program produced randomly generated sentences which " . . . were for the most part quite grammatical, though of course nonsensical." 56 Grammar validation by means of randomly generated sentences via computer had been an interest of Yngve, and others who worked with him, since 1957, according 51

W. N. Locke and V. H. Yngve, "Research in Translation by Machine at M. I. T."' Proceedings of the Eighth International Congress of Linguists (Oslo: 1958), pp. 510 — 514. 55 Yngve, Random Generation of English Sentences, p. 9. 56 Yngve.

INTRODUCTION

23

to an internal memorandum, dated December 7 of t h a t year, of the Mechanical Translation Group at the Massachusetts Institute of Technology. 57 That memorandum announced plans to automate Chomskyan-type grammar rules making use of a random number generator be a part of the COMIT programming system. Also in 1961, Sydney Lamb suggested the use of a computer for linguistic analysis in descriptive and comparative-historical linguistics, admonishing linguists t h a t they must be still more careful in their work, " . . . since the machine makes possible a means of testing formulations very objectively and with a severity which has never before been possible. 58 Paul L. Garvin subsequently discussed uses of the computer in linguistic research, categorizing the tasks to be performed by the computer as (1) to collect linguistic data, (2) to act on the results of linguistic research, and (3) to automate linguistic research procedures, and of these three tasks, Garvin gave most importance to the second. 59 The computer application in this work comes under the second category of tasks listed by Garvin. The computer acted on the results of this research by automatically generating kernel sentences and performing various transformations. The computer was used for this project to provide an impartial means for validating, or evaluating, the results of the operation of a generative bilingual grammar of Portuguese and English syntax. Errors in the output were put to work to improve the grammar. Bad output meant a mistake in the grammar or in the computer program automating the grammar, or possibly a mistake in both. J u s t as Sol Saporta and Heles Contreras set up criteria for the validation of a phonological grammar 6 0 and subsequently devised a transformational grammar of Spanish phonology to be subjected to these criteria, 61 this work makes use of a similar validation procedure. Saporta and Contreras proposed to submit output in random order to informants and depend on their responses to support or invalidate their grammar. 62 This grammar is valid to the extent t h a t native speaker intuition will accept its output, subject to conditions stated earlier. 57

According to a letter from Yngve to the writer. Sydney M. Lamb, "The Digital Computer as an Aid in Linguistics", Language, Vol. 37, No. 3 (July-September, 1961), p. 410. 69 Paul L. Garvin, "Computer Participation in Linguistic Research", Language, Vol. 38, No. 4 (October—December, 1962), pp. 385 — 86. 80 Heles Contreras and Sol Saporta, "The Validation of a Phonological Grammar", Lingua, Vol. IX, No. 1 (March, 1960), pp. 1 — 15. 61 Sol Saporta and Heles Contreras, Phonological Grammar of Spanish (Seattle: University of Washington Press, 1962). 62 Saporta and Contrex-as, p. 40. 68

24

INTRODUCTION

1.7. SOME COMMENTS ON THE COMPUTER PROGRAM

The computer program which automated this grammar consisted of approximately 18,000 instructions written in the F A P (Fortran Assembly Program) language. That was the most accessible language at the time this project was begun, and the writer stuck with t h a t language even after other more suitable programming languages became available, such as COMIT, METEOR, and SNOBOL. Were the writer to begin another similar project or continue this one, he would program in SNOBOL 4. The program in F A P consisted of a kernel-generating routine, a one-string transformation routine, a two-string transformation routine, and a number of subroutines used by the various routines mentioned. The two most complicated subroutines were those which generated noun phrases and verb phrases. I n addition to the noun categories established in the formal grammar, some semantic classifications were made in the program to increase the possibility of normal-appearing output. Nouns were classified as human, animate but not human, inanimate, and abstract. These nouns then selected certain verbs in various verb tables. Since only small stocks of nouns and adjectives were included in the program, it did not seem justifiable to include a routine to find the end boundary of each noun and then either add a pluralizing morpheme or cut off that morphene and make any necessary adjustments to have a singular form. Machine time was conserved by simply setting up short lists of singular and plural nouns and adjectives. The same was true where adjective gender and number were involved. The verb phrase routine selected a transitive, equational, or intransitive root verb, selected any modal, perfect, or progressive elements, conjugated the necessary element in accordance with the number and person of the subject, and then went on to develop the rest of the verb phrase, if the root verb was not intransitive, an indirect object and/or direct object for a transitive root verb, a complement for an equational verb. The kernel-generating routine selected a subject, making use of the noun phrase routine; then it used the verb phrase routine, and then it transferred to a routine which decided on any and how many time, place, or manner elements would be included in the kernel. The kernel elements, as they were generated, were placed in appropriate sentence slots located in core storage. After the kernel was generated, a routine examined each kernel element slot and recorded filled slots onto tape for printing. The one-string transformation routine started with the kernel as input, and thereafter the input was the result of the preceding transformation. The contents of the kernel slots underwent changes during the application of the one-string transformations. Some slots were blanked, others were filled, and still others had their contents replaced. As in the case of the kernels, a t

INTRODUCTION

25

tho end of each one-string transformation a routine examined the sentence slots, recording onto tape those which were filled. The compound-alternate one-string transformation was randomly applied more than once when there were time, place, or manner expressions present. The two-string transformation routine used as the input for each transformation the kernel sentence as one string (or a kernel having been subjected to the interrogative transformation) and either generated a second string (identified in the print-out as a "derived string") or used a ready-made stock kernel (identified as an "additional string") for the second string. Again a routine examined each sentence slot, recording onto tape only the filled slots. Because the kernel and the results of the one-string interrogative transformation were used as the input in two-string transformations, the kernel and interrogative strings were stored in the computer for safe keeping before the sentence slots were altered by the one-string transformations. When needed, those strings were called out of their keeping place and put back into the appropriate sentence slots for use by the two-string transformations. The computer program performed all one-string and two-string transformations in the grammar except for coordination, which was of little interest for the purpose of testing the grammar and which would seldom produce any degree of semantic compatibility. Also, some optional transformations following one and two-string transformations were not executed by the computer program. All optional choices were made randomly through the device of reference tables of choices compiled from cards which were physically shuffled in a random manner between computer runs. There were two-way and three-way choice tables. Four-way, five-way, six-way, and seven-way choices were made by successive references to the two and three-way choice tables.

1.8. POSSIBLE PRACTICAL APPLICATIONS OF THIS GRAMMAR

While this work is presented only as a tentative and incomplete model of a bilingual grammar of Portuguese and English syntax, perhaps it is permissible to suggest possible practical applications. The writer's immediate interest lies in making use of this grammar as the basis for the preparation of pedagogical materials. This grammar could be used to prepare materials for American students of Brazilian Portuguese or for Brazilian students of American English. Also, an adaptation of this format and an extension of the grammar's domain might provide a convenient reference grammar of one or the other language, or of both.

26

INTRODUCTION

Apart from pedagogical applications, the writer hopes that this work might be of relevance in the area of automatic parsing of either or both languages, and he hopes that this grammar might be useful in the construction of transfer grammars from Portuguese to English and from English to Portuguese. Further, the possibility seems to exist t h a t an automated grammar could be of interest in connection with research in psycholinguistics. George A. Miller has asked: "Are these systems of [Chomskyan syntactic] rules nothing more than a convenient way to summarize linguistic data, or do they also have some relevance for the psychological processes involved?" 6 3 He answers the second part of his own question in the affirmative. While making it clear t h a t the most elegant linguistic description does not necessarily describe the psychological processes which take place in the speech act, Miller asserts t h a t "an experimental approach to these matters is both possible and (potentially) rewarding." 84 Working on the assumption that the more complicated a grammatical transformation is the longer a human subject requires to go from a kernel to the transformed kernel, Miller and two other psychologists carried out research which produced results apparently supporting t h a t assumption. 65 With the negative transformation requiring the least time for performance, followed by the passive transformation, and the negativepassive, in t h a t order, Miller states: "In their gross outline, therefore, these d a t a support the transformational theorists." 66 Later research tended to indicate t h a t in addition to syntactic rules, semantic rules are also involved in sentence perception or intelligibility, 67 and subsequently a test was devised to isolate syntactic rules and semantic rules in learning tests. As anticipated, the results demonstrated "a differentiation between semantic and syntactic factors and a facilitory effect of both in learning.' '68 Experimentation conducted by Jacques Mehler led him to suggest t h a t subjects "do not recall a sentence verbatim, but rather that they analyze it syntactically and encode it as a kernel sentence plus appropriate transformation." 69 83

George A. Miller, "Some Psychological Studies of Grammar", American PsycholoVol. 17, No. 11 (November, 1962), p. 750. Miller, pp. 7 5 6 - 5 7 . Miller, pp. 757—59. Miller, p. 759. 67 George A. Miller and Stephen Isard, "Some Perceptual Consequences of Linguistic Rules", Journal of Verbal Learning and Verbal Behavior, Vol. 2, No. 3 (September, 1963), pp. 2 1 7 - 2 8 . 68 Lawrence E. Marks and George A. Miller, "The Role of Semantic and Syntactic Constraints in the Memorization of English Sentences", Journal of Verbal Learning and Verbal Behavior, Vol. 3, No. 1 (February, 1964), p. 4 69 Jacques Mehler, "Some Effects of Grammatical Transformations on the Recall of English Sentences", Journal of Verbal Learning and Verbal Behavior, Vol. 2, No. 4 (November, 1963), p. 350.

gist, •4 " «•

INTRODUCTION

27

Experimentation in psycholinguistics is cited here to support the suggestion that an automated grammar might be of interest in that area of research. The suggestion is made that an attempt might be made to find a correlation between the time required by a human subject to perform various transformations and the time required for an efficient computer program to effect the same transformations. Also, it is suggested that bilingual transformational grammars might be used in psycholinguistic research tc further test hypotheses stated for English assumed to hold claims for universality.

II PHRASE

2.1. S

— Nue + IP

2.2. Nue

-

2.3.

Nucx

-

2.4.

Subj

2.5. Nom 2.6.

NUCJ NUC2

Subj + Pred Nom ProNomsubj ProNomindef NP PN

-

NUP NCP NplP

NP

2.7. NUP

-

DPU + Nu

2.8. NCP

-

[NcPBg N c P pl

- >

DP

2.10. N c P pl

—

DP

2.11. Np,P

-

2.12. Nu

-

DPpi + Npl (P)NU (E)NU

2.13. (P)NU

-

2.14. mNu

-

m

2.15. f N u

-

f

2.16. (E)NU

—

(E)NUi, (E)N,

2.9.

N c P sg

sg + NCsg

PI +

c

N pl

1 1

ImNu LfNu N

N .

STRUCTURES

PHRASE

2.17.

STRUCTURES

leite; (E)N Ui -»• milk

mN

""li,

àgua; (E)N Ui —2p

Plßp

(E)Poss lp (E)Poss 2p (E)Poss Sp Let x = superscripts m and f. 2.61.

2pl

x Pos,

9»lp

xPos, x Pos xPo8

xPos,

Sglp/8g

sg2p

xPos,

P'ip

x Pos,

P'2p

"Pos,3

(E)Poss lp L (E)Poss 3p

selp/pl

s«2p/8g

x Pos, x Pos

«*2p/pl

P'ip/Sg p'ip/pl

34

PHRASE

STRUCTURES

x

Pos

x

Pos.P'2p/pl

pl

2p/sg

(E)Poss lp/9g (E)Poss lp/pl (E)Poss3p/9g (E)Poss3p/pl 2.62.

Pos, Pos, «2p/pl XP08, 2p/sg x Pos P>2p/pl x

x

x

x

a

pI

8g

Pos

ag

2p/for

x

Pos;

x

Pos,

x

Pos

x

Pos,P'2p/for

s

x x

«2p/pVfam

ag

Pos

2p/for

P'2p/8g/fam

P'2p/pl7fam

Pos,

P'2p/for

(E)Poss3p/8g/m (E)Poss3p/sg/f (E)Possâp/ag/n

2.63. (E)Poss3p/8g

ÎCardBg [Cardpl

2.64. (P)Card 2.65. Carda_

PoS| 2p/9g/fam

N„8g fN„ m

Card3( rCard„„ m

N,°»g N„"Bg

m f

Card,dual Card,plu

2.66. Cardpi Let x = subscript c or null. 2.67. Card dual

N,x

m

pi

pi

x

2.68. Cardplu 2.69. (E)Card

rmCarddual LfCarddual Cardplu,

JI

W.xpi

N, pi x

(EJCardi, (E)Card2> (E)Card3

2.70. (E)Quan

(E)Quansg (E)Quanpl

2.71. (EJIndefi

(E)Indef lsg (E)Indef lpl

PHRASE STRUCTURES

Let x = any other D P elements or null.

(E)Card ^ (EJCart^.

2.72. Optional English Transformation (E)Def (E)Dem (E)Poss (E)Def (E)Dem (E)Poss

(

(E)Alter] ) (E)Card (E)Ord ) (E)Alter ) (E)Card (E)Ord

(E)Def (E)Alter (E)Dem (E)Ord (E)Poss Let x and x ' = any other D P elements or null. x + (E)Card + of

2.73. Optional English Transformation (E)Def " x + (E)Incl x + (E)Incl+ of (E)Dem (E)Poss 2.74. ((E)Aprox) (E)Alter + (E)Card — ((E)Aprox)

{

another

-f- (E)Card

(E)Card + (E)Alter

2.75. (P)Approx

(P)Approxj, (P)Approx 2 . . .

2.76. (E)Approx

(E)Approx 1 , (E)Approx 2 . . .

2.77. (P)Approx 1

quase; (E)Approx 1 —>• almost ¡nearly

2.78. (P)Approxo

aproximadamente;

2.79. (P)Sep 2.80.

m

Quan s g

f

f

m

m

Quan p ,

f

(E)Approx 2 —- approximately

cada; (E)Sep ->• each/every m

2.81. Quan 3g 2.82.

m

Quan 9gi ,

Quan 8 g i

f

Quan 9gi , Quan s g ¡ m

Quan p l i ,

Quan p l j

2.83. Quan p l

f

2.84. (E)Quan sg

(E)Quan sgi , (E)Quan s g i

2.85.

m

Quan s g i

2.86. f Quan ä g i 2.87.

m

Quan 9g2

2.88. f Quan s g i 2.89.

m

Quan p l

f

(E)Def (E)Dem (E)Poss

f

Quan p V Quan pU

muito; (E)Quan 9gi muita;

much/a lot of

"

pouco; (E)Quan 9gi pouca; " m

m

Quan p l i ,

Quan p l t

2.90. Quan p l

f

2.91. (E)Quan pl

(E)Quan pli , (E)Quan pl>

f

Quan p l i , Quan p l j

little

PHRASE Quanpli

muitos;

( E ) Q u a n p l i —•

multas;

"

poucos;

( E ) Q u a n p l s —>

2.92.

m

2.93.

f

2.94.

m

2.95.

'Quanpi,

2.96.

m

2.97.

f

2.98.

m

2.99.

fIncl

2.100.

m

2.101.

fDef

2.102.

m

2.103.

f

2.104.

m

2.105.

f

2.106.

m

2.107.

f

2.108.

m

2.109.

f

2.110.

m

2.111.

f

2.112.

m

2.113.

f

2.114.

m

2.115.

f

2.116.

m

2.117.

fPos„„

minha;

rapospl

meus;

2.118. 2.119.

Quanpli Quanpl¡

STRTTCTURES

manyja lot of few

-perneas; " ( E ) I n c l ->- all

todo;

Incl9g

toda; ''

Inclsg

todos; "

Inclpl

todas; "

pl

Def9g

o;

( E ) D e f —>-

the

a; "

ag

os; "

Defpl

as; "

Defpl

este; (E)Dem j 9 g —• this

Demlsg

esta; "

Demlag

estes;

Demlp]

these

estas; "

DemlpI

ésse; (E)Dem 2 s g —> that

Dem2sg

essa; "

Dem2sg Dera33g

Dem3sg

agüele;

"

agüela;

"

ésses;

Dem2pl

( E ) D e m 2 p l —•

those

essas; "

Dem2pl Dem3pl

Dem3pl P o s „8g, lp/3g

lp/3g

fP0SP'lp/s

( E ) D e m l p l —>-

g

aqueles;

"

agüelas;

"

(E)Posslp/ag

meu;

" "

minhas;

"

2.120.

" P o s «sg, lp/pl

nosso;

2.121.

fPos

nossa; "

2.122.

m

2.123.

fPos

2.124.

mPos

o„ 9«lp/pl

PosDl p'lp/pl n,

( E ) P o s s l p / p l —>-

nossos; "

P'lp/pl

nossas;

ot,

teu;

®2p/sg/fam

my

"

( E ) P o s s 2 p —>- your

our

PHRASE

2.125.

Pos 882p/sg/fam

tua; "

2.126.

"Tos,,P^p/sg/fam

teus; "

2.127.

f

P()SP^p/sg/fam

tuas;

'Pos,,B®2p/pl/fam

vosso;

Pos a8g 2p/pl/fam

vossa; "

2.128.

2.129.

f

2.130.

m

2.131.

fpnu

2.132.

m

2.133.

f

2.134.

m

2.135.

f

Pos nP'2p/pl/fam

vossas; " seu; "

Pos a8g, 2p/for

sua; "

Pos„P>2p/for

seus; "

Pos'P'2p/for

suas;

PoS0„„ f

vossos; "

PosQ15g 2p/for

2.136. 2.137.

P^p/pVfam

Pos,s®3p

2.138.

mPos

2.139.

tPOS

Pi3p

Pl3p

2.140.

m

2.141.

f

2.142.

m

2.143.

f

2.144.

m

2.145.

'Ord^

2.146.

m

2.147.

f

Alter flg

Alter s g Alter p I

Alter p I Ord 8 g Ord p l

Ord p l

37

STRUCTURES

"

seu;

(E)Poss Sp/sg/m (E)Poss 3p/9g/f (E)Poss 3p/8g/n (E)Poss 3p/pI

sua; "

— his —• her — its ^ their

seus; sitas; " outro; outra;

(E)Alter

other

)>

outros; > y outras; a m

Ord s g i ,

m

m O r d s g s . . . Ord s g j

Ord sgil f Ord sg2 . . • f Ord 9g , m m Ord p I i > m Ord p U . . . Ord pl>

f

f

Ord pIi , f O r d p I j . . • f Ord p l j (E)Ord 1 , (E)Ord 2 . . . (E)Ord 3

2.148.

(E)Ord

2.149.

m

2.150.

f

2.151.

m

2.152.

f

2.153.

m

Ord s g j

segundo;

(E)Ord 2 —>- second

2.154.

f (

>dsg,

segunda;

"

2.155.

m

Ordp Is

segundos;

Ord 9g ,

primeiro;

(E)Ord 1

Ord 8gi

primeira;

"

Ord p l i

Ord pl ,

primeiros;

"

primeiras;

"

"

first

38

PHRASE STRUCTURES

2.156.

f Ord plt

2.157.

m Ord sg ,

último;

2.158.

fOrdsg>

última;

2.159.

mOrdpl.

•* últimos;

"

2.160.

f Ord pI .

-v

"

2.161.

m Indef lBg

um;

2.162.

f Indef l a g

urna;

2.163.

m Indef l p l

->- VMS; (E)Indef l p l

2.164.

f Indef l p I

-+ urnas;

2.165.

m Indef 2ag

->- algum;

2.166.

f Indef 2 s g

alguma;

2.167.

m Indef 2 p l

alguns;

2.168.

f Indef 2 p l

algumas;

2.169.

Indef 3sg

-> qualquer;

2.170.

Indef 3pl

-»

2.171.

™CardBg

->- um; (EJCardj

2.172.

'Card Bg

->- urna;

"

2.173.

m Card duaI

-• tres;

2.176.

PN

2.177.

PNBg

2.178.

PN pl

(P)PN pl (E)PN pI

2.179.

(P)PN gg

m PN sg fPN„,

->- segundas;

" (E)Ord 3 —• last

últimas;

(E)Indef lBg -*• ajsome " some

" (E)Indef 2 ->• some " " " (E)Indef 3

quaisquer;

duos;

any/whatever¡whichever

" —>- one

" (E)Card 3

three

ÍPN„,Sg PN pl (P)PN sg (E)PN eg

sg

m PN.

pi

2.180.

(P)PN p ]

2.181.

m PN

PN,pi "PN

2.182.

f PN sg

f PN sgi , f PN 9gi , fPNBg>, fPNsg f PN ggi , fPN8g
, (E)PN sg) , (E)PN sgi , (E)PNsg (E)PNBg?, (E)PN9g a senhorita Martins; (E)PN sg; —>- Miss Martins — a senhora Martins; (E)PN sgj —• Mrs. Martins

2.195.

f

PN

- >

2.196.

f

PN

2.197.

f

PN

2.198.

m

2.199.

f

PNpl

- -

f

PNpl

PNpIi.. .

PN p l ,. . .

José

Silva;

(E)PN ggi —>- Joseph Silva

— o senhor Silva; (E)PNBgt ->• Mr. Silva — dom José; (E)PNBgj -»- Mr. last name

P N Bg»

1

o Peru; (E)PN sgi — Peru

A PN

^ "g! PN

-

Ana Martins; (E)PN8g> ->• Ann

dona Ana; (E)PN8gio —• [Miss Miss 11 last name Mrs. j a Asia; (E)PN sgii

PNpI

-

(E)PN pli

ProNom vx ProNom,

ProNom x

' women

lp 2p

ProNom x 3p 2.201.

ProNom x i p

2.202.

ProNom x lp/sg

2.203.

ProNom x

2.204.

ProNom x

ProNom.X

lp/Sg

ProNom Xip/pl j(P)ProNom Xip/8g |(E)ProNom Xip/sg (P)ProNom xx

ip/pi

(E)ProNom.x

ip/pi ip/pi

(P)ProNom x 2p 2p

Souza;

the Souza

Let x = any first order subscript except indef.

2.200.

Asia

os (senhores) Souza; (E)PN plj ->• the Souzas

— as rsenhoritas \senhoras

PN p I ,

Martins

(E)ProNom x 2p

40

PHRASE STBUCTUBES

2.205.

(P)ProNom x

2.206.

ProNom x 2p/sg

2.207.

ProNom vx„2p/pl ,,

2.208.

ProNom vx 2p/sg ProNom x 2 p / p l iProNom, iPr("-"""x2p/Sg/fam |ProNom vx 2p/sg/for ProNom, 2 p/l»iy fa ill ProNom, 2p/pI/for

ProNom x 2p/sg/fam ProNom x 2p/pl/fam

ProNom..A 2p/8g/fam, ProNom.,A 2p/sg/fam, ProNom x 2p/pl/fam, ProNom x 2 p / p l / f a m ,

Let x : 2.209.

any first order subscript cxcept io or refi. ProNom,.x 2p/sg/for

ProNom x 2p/sg/for„

ProNom x 2p/pI/for

ProNom vi 2p/8g/forj'

¡

ProNom,. , „„ 2p/pl/forln ProNom,.xx 2p/pl/for f

Let x

subj or prep subscript.

2.210.

ProNom, 2p/sg/for ProNom x 2p/pl/for

f

ProNom x

f

ProNom x

2p/sg/forf i 2p/sg/forfi

ProNom x 2p/pl/for

fi

ProNom x 2 p / p l / f o r

ft

Ì Let x 2.211.

any first order subscript except indef. ProNom,. 3p

ProNom,.x 3p/sg ProNom X3p/pI

2.212.

ProNom x 3p/sg

(P)ProNom X3p/6g (E)ProNom xx 3p/sg

2.213.

ProNom..x 3 p / p l

2.214.

ProNoni:indef

((P)ProNom x 3p/pl ((E)ProNom X3p/pl (P)ProNom indcf (E)ProNom indef

41

PHRASE STRUCTURES

2.215.

(P)ProNom indef

2.216.

(E)ProNom indcf

mProNom

indef/h

mProNom

indel/inh

(E)ProNomindef/h (E)ProNomindef/inh "ProNom.

sub i3p/8g/h

2.217.

fProNom,

(P)ProNom8Ubj3p/sg

aub '3p/ag/h

ProNom,

sub Î3p/sg/inh

"ProNom 2.218.

ProNom

(P)ProNomsubj3p/pl

subj3p/pl/h

sub )3p/pl/h

ProNom.

8 U b i3p/pyinh

Let x = any first order subscript exccpt indef. ProNomv„3p/sg/m , , A

2.219.

ProNom..- 3p/»g/l

(E)ProNom. 3p/sg

x

x

ProNom..3p/sg/n x

2.220.

(P)ProNomsubjip/8g

eu; (E)ProNom subjip/sg -

2.221.

(P)ProNomäUbjip/pl

nos;

2.222.

ProNom.

2.223.

ProNomä

2.224.

ProNom

2.225.

ProNom

2.226.

ProNom

2.227.

ProNom

2.228.

ProNoms

2.229.

ProNom,

2.230.

ProNonij

2.231.

ProNom.

2.232.

"ProNom.

êle; ProNom,

2.233.

'ProNom

ela; ProNomsubjsp/gg/f — she

2.234.

ProNom,

0; ProNom6Ubj3p/sg/n -

it

2.235.

ProNom

0;(E)ProNom subj3p/pl

they

2.236.

"ProNom.

sub j'2p/8g/fam

1

i u b j2p/pl/fam,

(E)ProNom »ul>j

tu; (E)ProNom ssubj>>

2p

vos;

*

voce;

s u b i2p/pl/fam,

voces;

s u b j2p/sg/for

m

o senhor;

s u b i2p/pl/for

m

os senhor es; '

fi

a senhor ita;

fi

as

fz

a senhora;

fs

as senhoras;

s u b i2p/p]/for

u b j2p/sg/for

s u b i2p/pl/for

sub i3p/8g/h

sub j3p/sg/li

8Ub j3p/8g/inh

sub J3p/pl/inh

sub ':)y/pl/h

you

" "

senhoritas; " ' s u b j3p/sg/m

êles;

—• ice

>?

s u b i2p/8g/fam,

tUb i2p/sg/for

lp/pI

I

"

he

42

PHRASE

2.237.

f

2.238.

n,

2.239.

m

2.240.

Pred

STRUCTURES

ProNom s u b i3p/pl/h

elas; "

ProNom:indef/h

alguem;

(E)ProNom indef/h

someone ¡somebody

2.241.

ProNom lnde{/inh

algo; (E)ProNom indemnh ->

something

Aux + VP (Loc) (Tm V c + Comp V tria + DO (10) v

VP

trIb

V trl r ' a v

+ ProNom tefl 1 + Prep k verb

tr,b

Vc

2.243.

(P)VC

2.244.

V„

2.245.

Vc

2.246.

Comp

2.247.

ADJ

(P)V0 (E)VC v0l (E)VC

be-

esta-\ " Subj'

ADJ (Advdegl) Prc

Padj

Adj NP PN v ProNom prep

(P)Adv degl (E)Adv degl (PJAdv^68', (P)Adv 2 degl . . . (E)Adv1deBl, (E)Adv 2 d e g l . . . muito; (EJAdv/ 68 ' -»• very algo; (E)Adv 2 degl -»• somewhat

2.248.

Adv degl

2.249.

(P)Adv degl

2.250.

(E)Adv degl

2.251.

(PJAdv^ 8 '

2.252.

(P)Adv 2 degl

2.253.

Adj

2.254.

(P)Adj

2.255.

VCi + Comp

Vc> + ADJ

2.256.

V0> + (P)Adj

VCj + Adj temp

2.257.

V C i _,+ (P)Adj

VCi_, + Adjper

(P)Adj (E)Adj Adjpcr Ad

1DO

+ ProNomrefl + PrepTerb

^int 2.242.

(Man

jtemp

43

PHRASE STRUCTURES

Let x and its primes any other elements or null; let y = superscripts temp or per; m and f superscripts and subscripts are mutually exclusive. 2.258. x + Subj: r, "1 x " + Comp: [x'" + Adj y ] -f x " " — x' + m N m PN m ProNornm x' + f N f PN f ProNom f x -f Subj:

x' + m N m PN m ProNornm x' + f N f PN f ProNom f

x " + Comp: [x'"

m

Adj y f Adj y

Let x and its primes = any other elements or null; let y = Portuguese subscript pi or any combination of subscripts containing the subscripts pi. x' + N y x " + Comp: [x'" + Adj] + x " " PN y 2.259. x + Subj: ProNom y x' + N y x " + Comp: [x'" + Adj + -«] + x " " x + Subj: PN y ProNom y — mAdj per, mAdj2per 2.260. mAdjP« f 2.261. fAdjPer Adj, m temp m 2.262. Adj — Adj 2.263. 2.264.

f

Adj temp (E)Adj

f

2.265.

m

pesado; (E)Adjx —>- heavy

2.266.

f

*2.267.

m

2.268.

f

2.269.

m

2.270.

f

2.271.

m

Adj1

Adj1

per

Adj3

Adj2

per

per

pcr

Adj 1

Adj 1

temp

temp

Adj2 temp

Adj1temp, f Adj 2 temp . . .

(E)Adj1( (E)Adja, (E)Adj„ (E)Adj4 . . . pesada; " admirável;

(E)Adj2 ->- wonderful

admirável;

"

perdido; (E)Adj s -*• lost perdida; " quente; (E)Adj4 -»• hot

* For rules marked by an asterisk see additional notes on page 60

44 2.272. 2.273.

P H R A S E STRUCTURES f

Adj 2 t e m p

Pro

—»• quente;

"

(P)Prep a d j (E)Prep a d j

Padj

2.274.

(P)PrcPad,

(P)Pre P a d j i , (P)Prep a d j i , (P)Prep a d j s

2.275.

(E)Prep a d j

(E)Prep a d j V (E)Prep adjs , (E)Prep adJi

2.276.

(P)Prepadji

de; (E)Prep a d j i - of

2.277.

(P)Pre P a d j i

sem; (E)Prep a d j ! ->- without

2.278.

(P)Prep adj>

para; (E)Prep a d j t

2.279.

ProNom prep

2.280.

DO

2.281.

ProNom do

2.282.

Pr

(P)ProNom p r e p (E)ProNom o b j NP PN ProNom. 'do ProNom i n d e f (P)ProNom d o (E)ProNom o b j

ePverb + (P)ProNom d o •

P re Pverb + ProNom p r e p (P)ProNom tefl (E)ProNom refl

2.283.

ProNom refi

2.284.

IO

IO pll ProNom io

2.285.

IO

NP PN ProNom p r e p ProNom i n d e f / h

2.286.

Pre

2.287.

(P)Prep i o

2.288.

ProNom io

ph - Preps

Pio

2.289.

for

+

(P)Prep i0 (E)Prep l 0 para;

(E)Prepio

to/for

(P)ProNom i o (E)ProNom o b j

2.290.

( P ) V tr l f t

( E ) V tr, a (P)V t r i a , (P)V trias . . .

2.291.

< E ) V tr, a

(E)V t r i a , (E)V t r i a j . . .

2.292.

( P ) V tr l a i

nota-;

(E)V t

li

—>• notice-

PHRASE

STRUCTURES

promete-;

2.293.

(E)V t r

í(P)Vt,b

2.294.

V

2.295.

(P)yt,b

2.296.

(E)V

2.297.

(P)vtribi

2.298.

Prep v e r b

2.299.

v

trlb

F)ytrlb

V**, • • • queima-; (E)V t r i b -+ burn-

t r i b

(P)Prep verb (E)Pre P v e r b (P)Vtr,a

tr,

a

( E ) V tr, a

2.300.

(P) V tr, a

( P ) V tr, a i • • •

2.301.

(E)V t r ¡ a

(E)V tr , ai . . .

2.302.

(P)V t r ¡ a i +

(P)Prep verb -> consta-

(E)V t .

(E)Prep v e r b -* consist- of

+

de;

í

v**

2.303.

Vt r ¡

2.304.

(P)V tr , b

(P)Vtrlbl • • •

2.305.

(E)V tr2b

(E)Vt,bl * * *

b

*2.306. (P)V te (E)V te 2.307.

V:int

2.308.

(P)Vint (E)Vint (P)V¡ntl (P)Vintí (P)Vints (P)Vint)

2.309. 2.310. 3.311. 2.312. 2.313.

promise-

l( E ) V tr, b

de; + (P)Prep veri verb ->- recorda+ (E)Prep v e r b ->- remind- of |(P)V int (E)V int (P)V ínti , (P)V i n V (P)V intí , ( P ) V i n t i . . . (E)V l n V (E)V l n t f , (E)V int> , (E)V i n t < . . . vive- ; (E)V i n t i -> liveanda-; pesa-;

(E)V :int (E)V inti

fica- ; (E)V int
• as senhoritas; ' " PreP2p/pl/for

2.336.

ProNom prep2p/pi/for ^ ->

as senhoras; "

2.337.

ProNom da o 2p/sg/fam, , ,,

—

te; "

2.338.

ProNom da 0 2p/pVfam, , 1;f.

—

vos; "

(E)ProNom refl2p/pl (P)ProNomprepwm (P)ProNom preP9p/9g/f i(P)ProNom prep3p/pl/m (P)ProNom preP3p/pl/f (P)ProNom d03p/flg/m (P)ProNomd-

DO: ProNom do 10: ProNom,, DO: ProNom d o + 10 : IO p h *2.379. Aux

-

-[Te + Per] (Mod + Mkr inf ) (Perf + Mkr pap ) (Prog + -Mkr prp

2.380.

Mod

2.381.

(Pernod (E)V mod

2.382. 2.383.

(Pernod

,(E)V mod

2.384.

(P)Vm0(ll Mkr inf

2.385.

-(P)Mkr inf

(P)V modl • • • (E)Vm0(Il • • • pode-; (E)V modi

be- ablejcan-

-(P)Mkr inf (E)Mkr inf -r; (E)Mkr inf —• to

Let x and x ' = any other elements. 2.386

x + (E)Mod: can- + (E)Mkr inf + x' -» x + can + x '

2.387.

Perf

i(P)Vp(,rf (E)V perf

2.388.

(P)V perf

te-; (E)V perf -> have-

PHRASE

STRUCTURES

f(P)Mkr w |(E)Mkrpap

2.389.

Mki-pgp

2.390.

(P)Mkrpap

-do; (E)Mkrpap —»• -ed

-

Let x and x ' = any other elements or null. 2.391. x + „.V + (P)Mkrpap + x' - x + + V + -do + x ' 2.392.

x +

+ (E)Mkrpap + x '

2.393.

Prog

-

2.394.

(P)^prog

-

2.395.

(E)Vprog

-

(-PyVprog! • • • (E)Vprogi . . .

2.396.

(P^prog,

—

estd-; (E)Vprogi - be-

2.397.

Mkrprp

-

2.398.

(P)Mkrprp

—

i(P)Vprog (E)Vprog

(P)Mkrprp (E)Mkrprp -

ndo; (E)Mkrprp - * -ing

Let x and x ' = any other elements or null. 2.399.

x + „ V + (E)Mkrprp + x ' - x +

+V

+ -ng + x '

2.400.

Te

Pres Fut Past

2.401.

Fut

Futj Fut 2

2.402.

Fut x

2.403.

Fut 2

2.404.

Past

(P)Past (E)Past

2.405.

(P)Past

Past Completive Past Incompletive

2.406.

Past Completive

2.407.

Past Incompletive

-va-a-

2.408.

Per

(P)Per (E)Per

ftPJFut! |(E)Fut1

j(P)Fut2 |(E)Fut 2

0; (E)Past

-ed

50

PHRASE STRUCTURES

2.409.

(P)Per

2.410.

Hp 2p 3p

lp 2p 3p

.

_ 2.411.

2psg 2Ppi

2.412.

(E)Per

iPsg !p P i 2Psg 2Ppi 3p9g 3pP, 2Psg, 2P8g! 2PpU l2PPU

(E)3ps (E)Not 3pS|

Let x and x' = any other elements or null; let y = any subscripts except mod, perf, or prog. 2.413.

If Portuguese = x + -[Past Incompletive + Per] + (P)Vy + x ' Then English = -[(E)Past + Per]|fee- + [-ingr] + (E)V y (E)V y used to + (E)V y

Let x and x ' = any other elements or null; let y = any subscript. 2.414.

x -)- V y + Past Incompletive + x '

2.415.

x -f- .e_Vy + Past Incompletive + x ' x + .¿.Vy + Past Incompletive -f- x '

2.416.

x

-a-Vy L-i-Vy ,a.Vy L-i-^y .¿.Vy

Past Incompletive

x

-a-V' y -e-YT y

lp + x '

Past Completive Incompletive" IPpl + x ' Past Completive]

-va-a-

PHRASE

2.417.

-rei-re-râ-

(P)Futi

51

STRUCTURES

; (E)Futj — will

Let x and x ' = any other elements or null; let y = any subscript. 2.418.

x +

-[(E)Fut! + (E)Per] + (E)Vy + x ' — x + will -f (E)Vy + x'

2.419.

x + -[(P)Fut 2 + (P)Per] + (P)Vy + x' — x + i- + (P)Pres + (P)Per + (P)V y + (P)Mkr inf + x'

2.420.

x + x +

2.421.

x

2.422.

3p9g

0; (E)3p9K - -d

2.423.

l

l\e

0; (E)Not 3psg

2.424.

iPpi

-mos; "

2.425.

2Psei

-s; "

2.426.

2

0;

2.427.

2ppl,

-is; "

2.428.

2pPia

-m;

2.429.

3Ppi

-m; "

-[(E)Fut 2 ) + (E)Per] + (E)Vy + x ' — -[(E)Pres + (E)Per] + be- + going to + (E)Vy + x ' will going to

can -f x ' - > x

Psg,

will going to

be able + x '

"

"

Let x and x ' = any other elements. 2.430.

x

will (E)Past

(E)3p sg + x ' - x will 1 (E)Past J

Let x and x ' = any other elements or null; let y = any subscript. »2.431. x + ...Vy + (E)Pres + (E)3p9g + x ' - x + .fe.Vy + -s + x ' 2.432.

x

•a-V" "y

(P)Pres + lp 9g + x ' - x + ,0.Vy + x '

,.V y 2.433.

x -f

-f- (P)Pres

2

Ps gl 2Psg, 2 Ppi, 3p8g 3pPi

52

PHRASE

x + ,e.Vy + (P)Pres

STRUCTURES

2

P«gl

2

Ppis P sg 3pPi 3

2.434.

x +

2.435.

x

+Vy

+ (P)Pres + 2 Ppli + x ' - x +

+Vy

+ -s + x '

Past Incompleti ve -f- 2p pli + x ' -»•

-a-Vy L-i-Vy

+ x'

-ve-e-

L-i-Vy 2.436.

x

2.437.

x + V y + Past Completive

Past Completive + lp sg + x ' ->- x

-a.Vy

L-e-v y

2

Pb 8i

2

PPI, J

x + V y -ste -stes

2.438.

x

Past Completive

2

Psg, IX' Psg

3

-a-y* y -il + x '

tv: -0-

y

] y

2.439.

x + V y + Past Completive

2.440.

x + Vy + (P)Fut! T

2

Ppi, 3Ppi x' —

Psg iPpl 2 Ppli 2 Psgl 2Psg, L3psg j 2

PP>,

3

PP1 J

t' ->• x + Vy + -ram + x '

PHRASE STRUCTURES

-rei + iPsg -re- !Ppi 2Pp., „ •râ- 2Psg, " 2Psg, L3psg _ -rä- + -0 Let x and its primes = any other elements or null. 2.441.

x + Subj + x ' + (P)Per + x " ((P)ProNom refl ) x ' " (P)ProNom subjip/sg (P)ProNom 8Ubjip/pl ProNorn8Ubj2p/sg/fami ProNom8Ubj2p/pVfami ProNom,,8ubÌ2p/sg/fam, ProNom aubi'2p/pl/fam, ProNom,8UbÌ2p/8g/for ProNom,,,sabÌ2p/pl/for (P)ProNom8Ubl3p/sg (P)ProNom tadef NUP NcP8g (P)ProNom8Ubi3p/pi N c P pl N pl P

(P)ProNom refl lp/sg (P)ProNom refl lp/pl ProNom,"^p/sg/fam, ProNom re®2p/pl/fam, ProNom,refl2-3/s -pl g

iPsg !Pp. 2 P 8gl 2Ppii 2

Psg, 2Ppi, 2 Psg, 2Ppi, 3 Psg 3pP,

54 2.442.

P H R A S E STRUCTURES

x + Subj + x ' + (E)Per + x " (E)ProNom refl x ' " (E)Not 3p sg (E)3p 9g

(E)ProNom 6Ubjip/Bg (E)ProNom aubjip/pi (E)ProNom eubj2p (E)ProNom 9Ubi3p/pl NcPpl NplP ProNom 6Ubj3p/9g/m (E)ProNom indef/h ProNom 8Ubj3p/sg/f ProNom 8Ubj3p/Bg/n (E)ProNom indef/inh NUP N c-*-P sg

(E)ProNom reflip/sg (E)ProNom reflip/pI (E)ProNom refl2i)/sg (E)ProNom refl2p/pi E)ProNom refl3p/pl ProNom refl 3p/8g/m ProNom refl3p/8g/f ProNom refl3p/6g/n •2.443. x + Subj: x + Subj:

M ph/m

^ O Bg N P Wf C 8g \T C -p 8g h/m

^

1

PXh / f

N C 8g Prep loc 2.444.

x' + (E)ProNom refl + x '

Loc

Np l o c pj^loc

ProNom refl ,3p/sg/m ProNom refl 3p/sg/f

ProNom prep Advl0C Adv loc

PHRASE STRUCTURES i(P)Preploc l(E)Pre Pl0C

2.445.

Preploc

2.446.

(P)Preploc

-

(P)Prepl0Ci, (P)Prepl0Cj, (P)Preploc>. . .

2.447.

(E)Prepl0C

-

(E)Prepl0Ci, (E)Prepl0Cj, (E)Prep loc> . . .

2.448.

(P)PreploCi ->• atrâs;

2.449.

(P)Prepl0Ci

2.450.

(P)Prepl0C> —>•

(E)Prepl0Ci ->• behind

ao lado de; (E)Prepl0Cj —»• beside/next to (E)Preploc>

para;

to/toward

Let x = subscripts u, c, sg, or pl. *2.451 Nploo 2.452.

N„c

2.453.

(P)Nf

2.454.

m"M"loc

2.455.

fNloc

2.456.

(EX0C

2.457. 2.458.

m-M-loc 'u, fl\TlOO 00 NÎ.csg

2.460.

(P)NJ.0

2.461.

mNloc 8g fNloc °sg (E)N*loo !sg

2.463. 2.464. 2.465.

[fN!T m-M-loc ^u, • • • fvrloc •

2.459.

2.462.

i(P)Nf ((EJN^ jmNloc

mN'°

Sgl fNloc Sgl

Lot x - subscripts 2.466.

pj^loc

2.467.

(P)PN^0C

2.468.

mpvrloc JTl.'Xgg

• •

-

(E)Ni^, (E)Nj^ . . .

-

ar; (E)N^C — air âgua; (E)N„°C •(P)N.oo

water

(E)N'0C B 'g mN'0C csg

fN10C sg m-jq-loc °Sgl fNloc Sgl (E)N>°;g, (E)N^ g> . (E)Nf

prèdio; casa;

(E)^00Sga

or pl.

X loc ((E)PN!

|mpNloc

JfpNl0C

mpATloc sgi •

building house

56

PHRASE STRUCTURES

2.470.

(E)PN£

fpxrioB •*• cgi (E)PN^, ( E ) P N ^ . . .

2.471.

mPN;loc

o Brasil;

2.472.

fpfyTlOC

a Ásia;

2.473.

mpNloc

2.469.

2.474.

fpMloc x. -"eg

Sgl

•^•"«gi

fpNlpOC

fPN^

loc

(E)PN^

Brazil

( E ) P N ^ -> Asia ...

2.475.

(E)PNjy

(E)PNp,®, (E)PN'X • • •

2.476.

mpNloc

os Estados

2.477.

Unidos;

(E)PN 1 ^

(E)PNp°° —• the

as Filipinas;

the United

States

Philippines

i(P)Adv loc

2.478.

Adv loc

2.479.

(P)Adv ,oc

(P)Advi0C, (P)Advf, (P)Adv^, (P)Adv^0C, (P)Adv'50C.

2.480.

(E)Adv loc

(E)Advi0C, (E)Adv^0C. . .

2.481.

(P)Advi0C

aqui;

2.482.

(P)Adv!,0C

ca;

2.483.

(P)Adv»oc

ali;

(E)Adv^00

2.484.

(P)Adv'4oc

là;

"

2.485.

(P)Adv|°c

ai;

"

¡(E)Adv Ioc

(EJAdvi0® — here there

Pre P t m a + NP t m ' 2.486.

Tm

(Pre Ptmb ) NP t m ' Advtm (P)Pre P t m a

2.487.

PrePtma

2.488.

(P)Pre P t m a

(P)Prep tniai , (P)Pre Ptniai , (P)Prep t m a j .

2.489.

(E)Pre P t m a

(E)Prep tmai> (E)Prep tmat , (E)Prep t a a i

2.490.

(P)Pre P t m a j

depois de;

(E)Prep tm

2.491.

(P)Prep tma , )

antes de;

(E)Prep tm'a,

2.492.

(P)Prep tmai

em; (E)Prep t

2.493.

PrePtmt,

2.494.

(P)Prep tn , b

(P)Prep tmbi

2.495.

(E)Pre P t a b

(E)Pre P t m b i

2.496.

(P)Pn>Ptmb,

por;

(E)Pre Ptnia

-

ai

in

(P)Prep tmb (E)Prep tmb

(E)Pre P t m b i

for

after before

PHRASE STRUCTURES

Let x = subscripts u, c, sg, or pl. 2.497.

Nt m ' P v Î(P)N;tm, !sg (E)Ntm,

m"2.498. NÎu8g

2.499. 2.500. 2.501. 2.502. 2.503. 2.504.

mjjtmj csg fjjtm, 8g m-j^tm! csgi fj^tm, csgi

(P)N* n °eg C8g mi (E)Nî V ' 8g rajjtni! csg, fj^tm, C8gl

,

(E)N*?\ 8gj

(E)N'mi ... 8g |

veräo; (E)NÎ mi partida;

summer

(E)Nj™1

departure

Let x = subscripts u, c, sg, or pl. -Njtm, p 2.505. J^ptm, X ^X (P)N^ *2.506. N tm, 8g (E)Ntm, 2.507.

{P)Ntm.

2.509.

mj^tm, cBg fj^tm, ceg

2.510.

(E)N^

2.511.

mNtm, csgi fNtm, 8gi

2.508.

2.512.

mjjtm, csg f]SJ"tm, csg mj^tm, C8gl ' ' fN'm-. . . 8g i m % (E)N< m '. . . (E)N' V 8gi * 8g j dm; (E)N;mg; day

noite; (E)N^1

2.513.

Adv tm

Adv fut Adv pres Adv pa8t

2.514.

Adv fut

(P)Adv fut (E)Adv fut

2.515.

Adv pre8

i(P)Adv ptes [(E)Adv preB

2.516.

Adv pa6t

i(P)Adv pa8t [(E)Adv p a s t

2.517.

(P)Adv fut

(P)Advj ut . . .

— night

Oi

58

PHRASE

STRUCTURES

(E)Advj U t . . .

2.518.

(E)Adv f u t

-

2.519.

(P)Adv, u t

—

2.520.

(P)Adv p r e 8

^

(P)Adv p r e s . . .

2.521.

(E)Adv p r e s

-

(E)Adv pre8 . . .

2.522.

(P)AdvJ res

—

hoje; (E)Adv p r e s — today

2.523.

(P)Adv p a 8 t

—

(P)Adv p a 3 t . . .

2.524.

(E)Adv p a 8 t —

(E)Advj> a8t . . .

2.525.

(P)Adv p a 8 t

ontem;

->

(E)Advj u t -»- tomorrow

amanhä;

(E)Advf a s t ->- yesterday

Let x and its primes

any other elements or null.

2.526.

Adv t m + x "

x + Te + x'

2.527.

Man

2.528.

Prep m a n

2.529.

(P)Prep m a n

2.530.

(E)Prep m a n

2.531.

(P)Prep m a n i —•

Let x 2.532.

-

x

A d v pres

Pres Fut Past

Adv f u t Adv p a 8 t

1 Prep nman _Li xrpnian (Adv) degl f A d j mmaann + Su£f man [Adv A d v deg, jjpdeg J(P)Prep m a n |(E)Prepman

(P)Prep m a n i . . . -

(E)Prep m a n i . . . com;

(E)Prep m a n i

with

= subscripts u, c, sg, or pi. N™an PX.

NPman

»2.533. N™an

—

Tman i(P)N£ jman [(E)N£

2.534.

(P)N„ a n

2.535.

tubman

2.536.

firman

nvj^man f^man u n N!u, • • • fj^man

2.537.

(E)N„ a

( E ) ^ 1 , (E)N™an . . .

2.538.

mvrman

cuidado;

2.539.

ij^man öi

dificuldade;

2.540.

A d j man

->

(E)N™an — care

(P)Adj m a n (E)Adj m a n

(E)N£ a n -»

difficulty

PHRASE

2.541. 2.542.

(P)Adjm

f

(E)Adj i r

(E)Adjmani . . .

2.543.

f

2.544.

Suffman

2.545.

(P)Suff n

Adj r a a n i

59

STRUCTURES

Adjman] . . .

fâcil;

(E)Adjmani

easy

(P)Suff m a n (E)Suff m a n -mente;

-ly

(E)Suffman

L e t x a n d x ' = a n y o t h e r elements or null. 2.546.

x + (E) . . A d j ^ + (E)Suff m a n + x ' x + (E) .¿.Adj man + ( E ) S u f f m a n + x '

2.547.

Advman

|(P)Advman n [(E)Adv.man

2.548.

(P)Advman

(P)Adv5" a n . . .

2.549.

(E)Advman

(E)Adv™ an • • •

2.550.

(P)Adv5" an

bem; (EJAdv™" -* well

2.551.

Advdeg'

2.552.

(P)Adv d e g "

(P)Adv deB % ( P ) A d v d e g '

2.553.

(E)Adv d e g l

(E)Adv d e g -, ( E ) A d v d e g '

2.554.

(P)Adv d e g î

muito;

2.555.

(P)Adv

deg

(P)Advdeg' (E)Adv d e g "

(E)Advdeg'

(muito)

»

pouco;

L e t x = subscripts u, c, sg, or pi. *2.556. N P d e g 2.557.

Nfan

2.558.

(P)N2 u a n

2.559.

m

2.560.

f

N2 u a n

N2 u a n

2.561.

(E)N2 u a n

2.562.

(P)N« u g an

2.563.

m

2.564.

N?c u a n sg f N? u a n

-

NTanPv (P)Nfan (E)Nfan rn^quan fj^quan m-M-quan Ui • • • fj^quan ( E ) N qua n >

ln^quan c sg fj^-quan c sg rn^quan °Sgl fj^quan _

(E)Nqu

(very)

(E)Adv

much/a

degl

lot

( v e r y ) little

60

PHRASE

STRUCTURES

2.565.

(E)N^n

-*

( E ) N ^ n , (E)N«ugan . . .

2.566.

m

—

tempo; (E)N^ a n — time

2.567.

f

2.568.

,n

2.569.

f

N^ a n

N^ a n N«ugan

N?

uan

distância; -v

quilo;

(E)N^ a n

(E)Ncqaugan

milha; (E)Ni

Juan

distance

— kilogram — mile

If string includes DO: (P)ProNom do , 10: (P)ProNom io , or (P)ProNomrefl, 4.14. may be applied.

A D D I T I O N A L NOTES F O R C E R T A I N R U L E S 2.267. 2.306.

Irregular plurals of adjectives (and nouns) are not accounted for in this grammar. The computer program made use of (P)V t r ¡ h lembra- de, the semantic equivalent

2.339.

of which is (E)Vf, ria remember-. Not accounted for here are alternate post verb (stressed) second and third person direct object pronouns which correspond as follows: vocé ~ o, a; o senhor ~

o; a senhora, a senhorita ~ a; vocés ~ os, as; os senliores ~ os; as senhoras, as senhoritas ~ as; ele ~ o; ela ~ a; éles ~ os; elas ~ as.

2.379. 2.431. 2.443.

This g r a m m a r does not account for t h e recursiveness which produces verb phrases of the type 'is able to have been being able to have been . . .'. This rule makes most verbs ending in -y spell correctly, b u t not pay, toy, buy, etc. The superscripts h/m and h/f semantically designate human/masculino and human/feminine noun phrases.

2.451.

An example of N ^ P p i has not been identified.

2.498.

This begins a token rewriting of N^"'.

2.506. 2.533. 2.556.

This begins a token rewriting of Nx"''. man This begins a token rewriting of NT x N P d e g can replace Adv d e g : ' H e sang (songs) a lot/some time', ' H e threw the ball a lot/some distance', 'The book weighs a lot/a kilogram', ' H e walked the path a lot/three miles'. Although there are some complications, N P d e g is intended to m a k e unnecessary t h e "verbs of t h e middle" category.

Ill ONE-STRING GRAMMATICAL TRANSFORMATIONS

3.1. PASSIVE Let x and its primes = any other elements or null. 3.1.1.

x + Subj + x ' + V t r i a + DO

(10) x "

x + S u b j ' : DO + x ' + V pa98 + -Mkr p a p + V t . (10) (Prep a g + Ag: Subj) x " l(P)Vpa98 l(E)V pa98 se-; (E)V pa99

3.1.2.

y

3.1.3.

(P)Vpai

3.1.4.

PrePag

3.1.5.

x + S u b j ' : ProNom d o + x ' - li -f S u b j ' : ProNom s u b j + x '

3.1.6.

x + Ag: ProNom s u b j + x ' ^ x + Ag: ProNom p r e p + x '

3.1.7.

(P)Prep o g

pas»

-

be-

j(P)Prep a g j(E)Prep a g

por; (E)Prep a g

by

Rules 2.441 and 2.442 must be applied for subject-verb agreement, rule 4.9.29 for past participle agreement. 3.1.8.

Protuguese Optional Transformation Nom ProNom.s u b j 3 p x + Subj':

ProNom 8ub Ì2p/sg/fam., ProNom 3 u b Ì2p/p]/fani ProNom,subj

Aux + (P)V pa99 +

s

2 p / 9 g/for

ProNom,s u b Ì2p/pl/for

-Mkr p a p + V t r i a (IO)(Prep a g + Ag) x ' x + Aux + V t r

+ g e + S u b j ' (10) x '

-

62

ONE-STRING GRAMMATICAL

TRANSFORMATIONS

3.2. COMPOUND-ALTERNATE

Let x and x' = null or any elements not represented by y or v'; let y = Subj, DO, 10, Comp, Ag, Loc, Tm, or Man; let y' = a second occurrence of the same element represented b y y ; y a n d y ' ^ DO: ProNom refl or ProNom do , nor 10: ProNom i0 . Compnd 3.2.1. x + y + x ' ^ x + y Altern (P)Compnd (E)Compnd

3.2.2.

Compnd

3.2.3.

(P)Compnd

3.2.4.

Altern

3.2.5.

(P)Altern

e; (E)Compnd

and

(P)Altern (E)Altern ou; (E)Altern - or

If Subj transformed, apply rules 4.9.25, 4.9.26, 4.9.27, and 4.9.28 for subjectverb agreement; also rule 2.441 if ProNom refl in string, rules 2.258 and 2.259 for subject-complemet agreement, and rule 4.9.29 if passive for subject-past participle agreement. From this point Subj, DO, 10, Comp, Ag, Loc, Tm, and Man may also represent compound or alternate structures. 3.3. NEGATIVE

Let x and its primes = null or any elements not represented by y; let y = Subj, DO, 10, Comp, Ag, Loc, Tm, or Man; let y" = a second occurrence of the same element represented by y; let z = any subscript; let () = (P) or (E). 3.3.1.

x rgub.+

Pred

r()ProNom indef/h [()ProNom indef/lnh DP: ()Indef 2 (Alter) (Compnd) - [Altern j y Subj + Negi + Pred "()ProNom lndef/h/neg ()ProNom indef/lnh/neg DP: Indef 2 n e g (Alter) Neg 2 + y + Neg 3 + y' If string contains DO: (P)ProNom d o , 10: (P)ProNom i0 , or (P)ProNom refl , 4.13 must be applied.

ONE-STRING GRAMMATICAL TRANSFORMATIONS

3.3.2.

x + Subj + x'

x + Subj + + Negl + x'

3.3.3.

x + Subj + + Negl + x'

x + Subj + + Negl + x'

3.3.4.

(P)ProNom lndef/h/neg (P)ProNom indef/inh/neg DP: (P)Indef 2 n e g (Alter)

Comp DO 10 Loc Tm Ag Comp DO 10 Loc Tm Ag Comp DO 10 Loc Tm Ag Comp DO 10 Loc Tm Ag

: x'

(P)ProNom Indef/h/neB (P)ProNom indef/inh/neg LDP: (P)Indef 2 n e g (Alter).

()ProNom lndef/h OProNom^ef^

DP: ()Indef 2 (Alter)

"()ProNom indef/h/neg ()Pi'oNom indef ,, nh/neg DP: ()Indef 2 (Alter)

x + Negl + x'

(E)ProNom indef/h/neg (E)ProNom indef7lnh/neg (E)Indef 2

x + Negl + x'

(E)ProNom indef/h/neg , (E)ProNom indef/inh/neg , L(E)Indef 2neg ,

-v

(P)Ne g l (E)Ne g l

3.3.5.

Negl

3.3.6.

(P)Ne g l

näo; (E)Ne g l —>- not

3.3.7.

Neg2

i(P)Neg 2 [(E)Neg 2

63

64

ONE-STRING GRAMMATICAL

3.3.8.

Neg 3

3.3.9.

(P)Neg 2

3.3.10.

TRANSFORMATIONS

(P)Neg 2 (E)Neg 3 nem; (E)Neg 2

neither

" ; (E)Neg 3 —• nor

3.3.11.

(P)ProNom i n d e f / h / n e g

ninguem; (E)ProNom i n d e f / h / n e g ->• no one I nobody

3.3.12.

(P)ProNom m d e f / l n h / n e g

nada; (E)ProNom lndef/ilJVneg — nothingjnot a thing

3.3.13.

Indef,2neg

3.3.14.

(P)Indef 2 n e g (E) Indef 2 n e g

3.3.15.

(P)Indef 2 s g / n e g

3.3.16.

(P)Indef 2 p l / n e g

(P)Indef 2 n e g (E)Indef 2 n e g (P)Indef 2 g g / n e g (P)Indef 2 p l / n e g (E)Indef 2 8 g / n e g (E) Indef 2 p l / n e g m f

Indef,2sg/neg Indef

j m Indef 2 p l / n e g [ f Indef 2 p l / n e g

Let x and its primes = any other elements or null. 3.3.17. x + Indef 2 n e g + x ' + (P)N + x " x i (P)Indef 2 s g / n e g [ (P)Indef 2 p V n e g

Nu N„

-

krl 3.3.18.

x

(P)Indef 2 a g / n e g (P)Indef 2 p l / n e g

Nu NP [N,V N,pi .

ONE-STRING GRAMMATICAL m f

Tnrlpf

65

"Nu mN„

Indef29g/neg l

f

TRANSFORMATIONS

Indef 2pl/neg Indef,2pl/neg

f f

N,u N,

ml N,cpi n Nr, rf-N V f N" 3.3.19. x + Indef 2 n e g + x ' + (E)N + x " (E)Indef 2sg/neg (E)Indef 2pl/neg

(E)NU [(E)NCsg f(E)NCpl i(E)Npl

3.3.20.

m

Indef 28g/neg

3.3.21.

m

Indef.2sst/neg

nenhuma;

"

3.3.22.

m

Indef,2pl/neg

iienhuns;

(E)Indef 2pl/neg -»• no

3.3.23.

f

Indef 2pl/neg

-«- nenhum;

nenhumas;

(E)Indef 29g/neg — no/not a

"

3.3.24. (E)ProNom indef/h/neg .

anyone} anybody

3.3.25. (E)ProNom lndef/inh/neg ,

anything

3.3.26. (E)Indef 2neg ,

any

Let x and x ' = any other elements or null; let y = any subscripts except mod, perf, prog, pass (unless Te = Fut), or aux; (E)V yi ^ be- when Te = Pres or Past; (E)Vy, = be- and (E)Vpa9S when Te = Pres or Past. 3.3.27. x + (E)Neg 1 + -[Te + Per]

x + -[Te + Per]

(E)V mod + (E)Mkr lnf (E)V perf (E)Vprog (E)V yi (E)V yj be- -f not + able to can -f- not have- -f not be- -(- not (E)VftUX -+- not -f- (E)V yi (E)V y , + not

66

ONE-STRING GRAMMATICAL

3.3.28. X + -[

Pres Fut Past

(E)Not 3psg (E)3 P s g (E)Per

TRANSFORMATIONS

] + (E)V aux + x '

x + (E)V aux : [ 1 do does I -[Pres + (E)Per] -(- be- -f- going to will did

] + x'

3.3.29. x + -[Pres + (E)Per] + be- + going to + not + x ' — x -(- -[Pres -(- (E)Per] + be-

not -)- going to -f- x'

Rule 4.13 must be applied for Portuguese pronoun placement.

3.4. INTERROGATIVE

Let x and its primes = any other elements or null. Interrogative Subj 3.4.1.

x + Subj + x ' - x + Q subj + x ' Rule 3.4.69 must be applied for subject-verb agreement.

3.4.2.

Interrogative Loc, Tm, Man, or Motive. x + Subj (x' + N e g ^ x " Loc Tm Man

0 Qloc

(x') Subj (Ne g l ) x " + x " '

Qtm

Qmau Qmot

Interrogative Comp 3.4.3.

x + Subj (x' + Neg 1 ) x " + Comp:

[

Subj' (Adv deg ) Adj P re Padj N P PN ProNom„

]x'"

ONE-STRING GRAMMATICAL TRANSFORMATIONS x

+ Qcomp* [ Qsubj Qcomp adj Qe tcomp prep

67

] (x') Subj (Ne g l ) x " + x "

Interrogative DO 3.4.4.

x + Subj (x' + Negj) x"(Prop v e r b ) DO + x ' " x (Prep vprb ) Q obj (x') Subj (Ne gl ) x " + x ' "

Interrogative 10 or Ag 3.4.5.

x + Subj (x' + Negi) x " 10 Prep ng + Ag Qio

(x') Subj (Negj) x " + x " '

LQ«„

Multiple Interrogative Let x and its primes = any other elements or null; let Y — Loc, Tm, Man, or Prep ag + Ag; let v =-- loc, tm, man, mot, or ag Q subscripts but Q y ^ Qy• quanto;

(E)Q q u a n ^ -*- how much

->

"

quanta;

—>• quantos;

(E)Q q u a n p l -* hoiv many

—• quantas;

"

quejqual; -+

(E)Q d e t —»• whatjwhich

que, quais;

"

70

ONE-STRING GRAMMATICAL TRANSFORMATIONS

Let x and x ' = any other elements or null; let y = superscripts loc, tm, man, or mot; let y ' = the corresponding subscripts. 3.4.52. x + QNP y -f x '

x + Prep y , + QNP y + x ' y

J(P)Pre Pmot |(E)Prepmot

3.4.53.

Prep mot

3.4.54.

(P)Prep mot

-

(P)Prep n)0ti . . .

3.4.55.

(E)Prep mot

-

(E)Prep niot] . . .

3.4.56.

(P)Prep moti

-

por; (E)Prep inoti — for ( p )N mot csg (E)N mot

3.4.57. N mot 3.4.58. (P)N™4 3.4.59.

m

ot N™ csg

3.4.60. fNccmot sg 3.4.61. (E)N™ in v mot 3.4.62. n, NJCSg, jmot 3.4.63. f N"c»Bi 3.4.64.

"Sgl 4

Q

(E)Nflriot. . . motivo; (E)N™ot -> motive razäo; (E)N"c lot ->• reason * ' sgi 0

Let x and its primes = any other elements or null; let y = any subscripts ( xcept mod, perf, prog, pass (unless Te = Fut); (E)Vyi ^ be- when Te — Pres or Past; (E)V yi = be- and (E)Vpa88 when Te = Pres or Past. 3.4.65. x

Qr Qcomp Qdo Qio Qloc Qtin Qman Qmot Qag Qact

Subj + -[Te + Per]

(E)V mod + (E)Mkr lnf (E)Vperf (E)Vprog (E)Vyi (B)V y ,

ONE-STRING

GRAMMATICAL

[Te + Per]

Qr

Q comp Qdo Qio Qloc

Qtm

71

TRANSFORMATIONS

be- + Subj + ohle to can -f Subj haveSubj be- + Subj (E)V aux + Subj + (E)V yi (E)V yi + Subj

Qman Qmot Qag Qact

Rule 3.3.28 must be applied if (E)V aux is in the string. Let x and x ' = any other elements; let (E)V y = any verb in right half of rule 3.3.65. 3.4.66. x + -[(E)Pres + (E)Per] + be- + going to + (E)V y + Subj + x ' — x + -[(E)Pres + (E)Per] + be- + Subj + going to + (E)V y + x ' Let x and its primes = any other elements or null; let X = (P)Per 1, 2 or 3; let X ' = another occurrence of (P)Per. 3.4.67.

x

x

+ Q8Ubj + x '

+ Qsubi +

x

'

r

A

sg 'X* X + X' B)3p„ (E)Not 3p sg )E)Per + (E)Per'

Tro^ 3pPi (E)3p 5g (E)Not3p S|

If string includes DO: (P)ProNom d o , 10: (P)ProNom io> or rule 4.13 must be applied.

(P)ProNom rell >

IV TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.1. MODIFICATION

Let x and its primes = any other elements or null in S; let y and y' — any other elements or null in S'. 4.1.1. S: x + x': [ NP ] x ' PN S':y

NP PN Aux -f Vc -f- Comp: Subj:

Aux + Vc + Prep adj NP PN Prep ag + Ag:

NP PN + Comp: NP PN

DO:

NP PN NP PN

PrePio Prepioc PrePtm

PrcPman S": x -f x': [

NP PN

]

ReLpronomsut,j

Y

Rel

A u x

pronom8Ubj pronom 8Ubj +

+

V

c + 7

Prepadj + Relpronomob. + y + Aux + Vc

TWO-STRING GRAMMATICAL TRANSFORMATIONS "D pi ivD1

y + y'

pronom ob j

Prep ag + Rel pronomobj -f- R6lprononi0},j loc

Rel Prep loc + Rel p r o noni o b j Rel tm Prep tm + Relpronomob. Rel man PrePman + ^elpronomobj

Rule 4.4.12. applies to occurrence of Rel tm in Portuguese. Portuguese Indefinite Clause 4.1.2.

x

Pres Fut Past

x': [NP: [DP: (P)Indef + x " ] + x " ' ]

y +

Rel pronom8ubj

0

Rel pronomobj

Prepi0 Prep ag Rel loc Rel tm Rel man -[

Pres Fut Past

Per] + y' + x " " —

Pres Fut Past

x': [NP: [DP: (P)Indef + x " ] + x ' " ]

74

TWO-STRING GRAMMATICAL TRANSFORMATIONS

Rcl p r o n o m 8 u b j

0

Rcl p r o n o m o b j

Pi'ePio Prep a g J Rcl , o e Rel t m Rel m a n -[

4.1.3.

Pres s b j c Past 8 b j c

Per] + y ' + x '

English Optional Transformation x + NP + (E)Prep 4- (E)Rel pronom + y + x ' x + N P ((E)Rel 1)rouom ) y + (E)Prep + x ' Rpl h

4.1.4.

Rel p r o n o m s u b j

4.1.5.

h R(>l ^pro»om8ubj

(P)Rel pronom (E)Rel pronoinsub .

4.1.6.

inli Rel pronom

4.1.7.

h "Rpl -Lvclpronomobj

(P)Rel pronom (E)RelpJ.„nom t)„]h

4.1.8.

h Rpl •Lvclpronomobj

4.1.9.

(P)Rel pronom

-Lvclprononisubj inh jPpl .vcipronom

iv^1pronom

obj

r> linh "•""^pronom

(P)Rel prononiobj (E)Rel prononJob . que; (E)Rel£ ronoinsubj (E)Rel ppronom "o

4.1.10. 4.1.11.

(P)Relp r o n o n i o b j

4.1.12.

Rel loc

4.1.13.

(P)Rel l o c

4.1.14.

Rel t m

4.1.15.

(P)Rel t m

4.1.16.

Rel man

4.1.17.

(P)Rel maI1

que;

whojthat

which/that

(E)Rel prononiob . j/iviivniQm

ivhom

(P)Rel l0C (E)Rel l o c onde;

(E)Rel l o c — where

(P)Rcl t m (E)Rel t m quando;

(E)Rel t m -»• when

(P)Rel m a n (E)Rel m a n como;

(E)Rol m a n + that

TWO-STRING GRAMMATICAL TRANSFORMATIONS

75

Lot x and x ' — any other elements or null in S; let y and its primes = any other elements or null in S ' ; let z = subscripts 2 a or 2 b . 4.1.18.

Portuguese Obligatory, English Optional Tranformation x + N P + Rei pronom o b j, + y + V t r , + y ' + Prep vcrb + y " + x ' x + N P + Prep verb + Rcl pronomob . + y + V trz + y ' + y " + x '

L e t x and x ' = any other elements. 4.1.19.

x + (P)Prep + ( P ) R < o n o n i o t . + x ' x + (P)Prep + (P)Rel^ ronomprep + x '

4.1.20.

(P)Relp ronomprei) —>- quem

L e t x and x ' — any other elements or null in S; let y and v ' — any other elements or null in S'. 4.1.21.

Portuguese Optional Transformation x + N P + y + (P)Rel pronom + y ' + x ' - , n i NP

0

que qual a que qual OS que quais as que quais

8g

fNP

w m NP pl f NP

4.1.22.

pl

y' + x'

Portuguese and English Optional Transformation x + NP + Rel pronon , sub . + y + V c + Comp: (Adv deg ) Adj Prep :ldj NP PN ProNom prep x + N P (Adv P r e

4.1.23.

g)

Padj

y' + x'

Adj NP PN ProNom„

x + NP + Prep a d j + D P : Def + N u + x ' -> x + NP + Prep a d j + N u + x '

76

TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.1.24.

(Advdeg) Adj L(E)Pre Padj : o f ^ + (E)ProNom obj "DP + (Advdeg)Adj (E)N + x' (E)Poss

X + DP +

(E)N

4.1.25. English Optional Transformation x + DP + (E)N Prep a d j + (E)NU (E)Prep adj : of 0 8 8 DP' (E)N' (E)PN DP + (E)NU DP' + (E)N' (E)PN

(E)N + x'

(E)N pl " -' + x' (E)PN pl Let x and x ' = any other elements or null in S; let y and y' = any other elements in S'; y' may be null in English. 4.1.26.

x

(E)Npl

[_(E)PNpl J

-'s

-f- x ' —>- X

4.1.27. Portuguese and English Optional Transformation x + NP + Rel prononigub . -f y + Prog + -Mkr prp V»,

y + x'

' int

x + NP + -Mkr.prp Vtl V,int

y' + x '

4.1.28. English Optional Transformation x + DP + (E)N + (E)Rel pronomsub . + -[Te + Per] + Prog + -Mkrprp + (E)Villt + x ' ^ x + DP + -Mkrprp + (E)Vint + (E)N + x' 4.2. S E N T E N C E AS S U B J E C T , COMPLEMENT, OR D I R E C T O B J E C T

Let x and its primes = any other elements or null in S. 4.2.1.

4.2.2.

4.2.3.

S' as Subj S: x - f Subj + x ' + Vs'"ubj x " => S' S " : x + x' + Vs'6ubj + x " + Rel conj + S' x + x ' + (E)V s 's«bi + x " + R e l c o n j + S ' x + i t + x ' + (E)V s 'Bubj + R e l c o n j + S ' V s '«ubj

VSc'sul>j yitsubj

-

TWO-STRING

GRAMMATICAL

(P)Vfsubj

4.2.4.

V®'»ubj

4.2.5.

(P)Vfsubj

(P)v®'»»"i, ( P ) v c s ; . . .

4.2.6.

(P)V»'Subj

se-;

4.2.7.

(P)v»;«ubj

está-; "

4.2.8.

yS'subj

4.2.9.

(E)Vf™bj

(E)Vf»ubj —

(E)VtS¿ubj

(P)V|«">J

•

•

•

s

(E)v t r ;subj...

4.2.11.

interessa-;

(P)Vts>bj

8ub

(E)V t ®^ubj

s (P)Vjn ;Subj

4.2.12. V^subi

interest-

(EJVj^ubi i

-•

(P)Vin?>.bi . . .

4.2.14. (E)V¡^«'>i

-• -•

(E)v t o ?;»'.j...

4.2.13. (P)Vi^

be-

(P)V®»1>J

4.2.10. (E)Vt®^ubi

4.2.15. (P)VIn?;-»bl 4.2.16.

77

TRANSFORMATIONS

Rel conj

4.2.17. (P)Rel conj

--

remain-

fiea-; (E)Vin®>bi i(P)Rel conj |(E)Rel c o n j que; (E)Rel conj

that

Let x and its primes = any other elements or null in S; let y and y ' — any other elements in S'. 4.2.18. x + -[Te + Per] + x ' + (P)V®™w + Comp: [(AdvdeE) Adj 9hic ] + x " + Rel conj + S'

-

Pres Per] x ' + (P)Vfa»bi + Comp: Fut Past g JC [(Adv )Adj;3bjc-| ] + x " + Rel conj + y + "Pressbic Past 9 b j c 9bjc 4.2.19. Pres -» 0 Let x and x ' = any other elements or null. 4.2.20. x + -[Pres 8bic -f Per]

(P)-0-V (P)-i-V

x + -[Per] 4.2.21.

Past 8 b j c

.(P)-a-V -sse-

P e r ' ] -f y '

78

TWO-STRING

Lot x and x'

a n y otlier e l e m e n t s or

4.2.22.

x +

-[Past

9bjc

GRAMMATICAL

-{- - w i o s ]

|TP)«

TRANSFORMATIONS

null. V

(P).,v (P)-i.V x +

-[]

(P).„,V (P).^7

4.2.23.

Adj9bic

4.2.24.

j c

4.2.25. 4.2.26. Let

Adjf Adj|

—

possivel;

A d j f

( E ) A d j ->•

bic

—> provdvel;

( E ) A d j —>•

ic

—• duvidoso;

(E)Adj

Adjjf

any

x and

x'

-

S'

as

Comp

S:

x +

4.2.27.

i c Adjfic, Adjf ,

-

other elements

V^ubj

Comp +

x'

possible

->•

probable doubtful

or null in

S.

=•

S' S":

Vcs>hJ +

x +

L e t x a n d its primes =

Relconj +

S'

a n y o t h e r e l e m e n t s or null in S; lot z - - a n y

exceptlb.

4.2.28.

S'

as

S:

x +

DO Vts,>

+

x

' +

DO +

x "

=>

S' S":

x +

Vt8r'do +

x' +

x" +

Relconj +

S'

vs'do 4.2.29.

vtsr>

fia V.fdo V S'do tr ! b

4.2.30.

V£

S': Q„r + y S " : x + Q n t + y + x ' + V 8 ' sub i

x

"

TWO-STRING GRAMMATICAL TRANSFORMATIONS

81

Interrogative as DO 4.3.2.

S: x + Vtsr'do

X'

+ DO +

x"

S': Qnr + 7

Let x let ns prog, 4.3.3.

S " : x + V*do + x ' + x " + Q nr + y --- any elements in S; let v and y ' = any other elements or null in S'; any Q subscript except subj; let z = any subscript except mod, perf, pass (unless Te = Pres or Past), or aux. x + Qn9 + -[Te + Per] y' be- + Subj + y + able to can -(- Subj + y E)Vperf+Subj+ y (E)V prog + Subj + y (E)V aux + Subj + y (E)VZ + Subj + y x + Q us + Subj + -[Te + Per] be- + y + able to can -f- y

E)V perf + y (E)prog + y

(E)V aux + y (E)VZ + y 4.3.4.

English Optional Transformation x + y + -[Te + Per] + (E)V aux + (E)V + y ' x + y + -[Te + Per] + (E)V + y ' Retort Interrogative as DO of Negative Let x and its primes = any other elements or null in S; let y = any elements in a non-negative retort interrogative. Retort Interrogative as DO of Negative 4.3.5. S: x + Neg x + V^r'Qr do + x ' + DO + x " =•

4.3.6. 4.3.7. 4.3.8. 4.3.9. Rule

S': Q r + y S " : x + Negj + V?;Qr do + x ' + x " + Rel Qr + y (P)Rel Qr Rel r (E)Rel Qr '(P)V£ Qr do V®r Qr do (E)V£ Qr do (P)Rel Qr if/whether se; (E)Rel Qr (P)V«'Qrdo sabe-; (E)V^' Q r d o —>• know4.3.3. must be applied.

82

TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.4. S AS TIME ELEMENT

Let x and x ' = any other elements or null in S. 4.4.1.

S: x + Tm + x ' => S' S " : x + x ' + Rel t m + S'

4.4.2.

J(P)Rel t m |(E)Rel t m

Rel tm

Let x and x ' = any other elements in S; let y and y ' = any other elements in S'. 4.4.3.

x + Te + x ' + Rel t m + y + Te + y ' x ' + Rel t m + y

'Pres Fut Past 4.4.4.

Pres' Pres' Fut' Past'

x + F u t + x ' + (P)Rel t m + y [Pres 1 y ' [Fut' X + F u t + X'

lpres sbjc I"(P)Rel^ m

[(P)ReCsb* 4.4.5.

-

(P)RelP™

sbjc

4.4.6.

(p)Relfutsbic

4.4.7.

(E)Rel t m

(p)Relpres

sfcjc

J

Pres Fut

(P)Rel™ 9 b i c , ( P ) R e l ^ 9 b i ° . (P)Rel^, f l b i c • • • (E)Rel tmi> (E)Rel tmi> (E)Rel t a a , (E)Rel tm< . . .

(P)Rel^

9bic

antes que; (E)Rel t m i ->• before

4.4.9.

(P)Rel^

0

quando; (E)Rel t m i —»• when

4.4.10.

(P)Rel™ 9 b j c

4.4.8.

4.4.11. Let x : 4.4.12.

(P)Relfutj9bic

depois que; (E)Rel tm> logo que; (E)Rel t m j

as soon as

any elements in S; let y a n d y ' = any other elements in S'. (P)Rel^ eB sbjc 1 y I Pres (P)Rel™ 8 b j c ] [Fut (P)Rel t m J J (P)Rel^ e a 9bic 1 y rPres 9bjc (P)Rel™ 9 b j c l [Fut 8 b i c tm (P)Rel J J

4.4.13

after

Fut s b i e

-r-

Lot x and x ' = a n y other elements.

TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.4.14. x + Fut9bjc

2

P 9gl 3ppl

L 2PpU

-re-r-

2

Psg. 3ppl -des 4.5. S AS LOCATION ELEMENT

Let x and x' = any other elements or null in S. 4.5.1.

S: x + Loc + x' =» S' S": x + x' + Relloc + S'

4.6. S AS MANNER ELEMENT

4.6.1.

S: x + Man + x' => S' S": x + x' + Rel man + S'

4.6.2.

Rel man

4.6.3.

(E)Relm(m

(P)Rel man (E)Relman —

as/like

Let x = any elements in S; let y = any other elements in S'. 4.6.4.

Portuguese and English Optional Transformation x + Rel man + Subj + y ^ x + Rel man + Subj

4.6.5.

x + (E)Relman: as + Subj

x + like + Subj

4.7. OPTIONAL UNKNOWN RELATIVE TRANSFORMATIONS

Let x = any elements in S; let y and y' = any other elements in S'. 4.7.1.

Portuguese Optional Transformation. (P)Rell0C 1 y riPres 1 (P)Rel man J (Fut J Past (P)Rel loc I y rPres8bic ] y' (P)Relraan J [Past8bjc J

84 4.7.2.

TWO-STRING GRAMMATICAL TRANSFORMATIONS

English Optional Transformation (E)Rel t m (E)Rel l 0 C L(E)Rel man

whenever wherever however

y

4.8. SUBORDINATION

4.8.1.

S => S' S": S

^ O l l j purpose C o n

i n e g result proviso

Conj concession Conj con(Jition Conj c o n t r a r y f a c t Coni false man -

(P)Conj purpose (E)Conj p u r p o s e

4.8.2.

^ O t t j purpose

4.8.3.

(P)Conj purpose

(P)Conj purpoBei , (P)Conj purpose 2 '

4.8.4.

(E)Conj purp09e

4.8.5.

(P)Conj purposei

(E)Conj purpose1

4.8.6.

(P)Conj purp0Be2

4.8.7.

(P)Conj purpo8e>

4.8.8.

^ o n ] n e g result

4.8.9.

(E)Conj n e g

4.8.10.

(P)Conj neg

(P)Conj purposf . s . . . para

que;

(E)Conj purp09ei

afim que;

result

de modo que;

result result resultaffir.neg

1(E) Conj neg

result n e 2

4.8.11.

that

"

i(E)Conj n e g sem que;

in order

"

(P)Conj n e g (E)Conj neg

result

•• •

(E)Conj n e g

" ; (E)Conj neg

resuUaffjr i i e g

re9Ult

-

^

without

unless

Let x and x ' = any elements in S except Neg; let y = any elements in S'. 4.8.12.

x + (Neg) + x ' + (E)Conj n e g x -f x' x + Neg +

re9Ult

(E)Conj n e g x'

j(E)Conj n e g (E)Conj n e g

+ y

-

resultaffir.neg

resultaffir.negj result« „„

I

TWO-STRING

GRAMMATICAL

85

TRANSFORMATIONS

Rule 4.8.38 must be applied when (E)Conjneg re8Uitaffir neg i s

string.

(P)Conjproviso (E)Conjproviso

4.8.13. Conj proviso 4.8.14.

(P)Conjpr0Vi90

(P)Conjpr0vi80i, (P)Conjprovi80j, (P)Conj pr0vis0> ...

4.8.15.

(E)Conjpr0vi80

(E)Conjpr0Vi80i, (E)Conjprovia0i, (E)Conj provi80j ...

4.8.16. (P)Conjpr0Vl80i

contanto que; (E)Conjprovis0i

4.8.17. (P)Conjprovi80j

caso; (E)ConjprC)vi80j

4.8.18.

(P)Conjprovi80>

4.8.21.

(E)Conjconce8aitm

4.8.22.

(P)Conjconce98ioni

4.8.23.

(P)Conjconce8Bioni

4.8.24.

(P)Conjconce83ioilj

(P)Conjconce88ioni, (P)Conj concession 2 ' (P)Conjconce89ion> . . . (E)Conj conce88ioni . . . ainda que; (E)Conjconcessioili embora;

4.8.25. Conjcondltion 4.8.26.

se; (E)Conjcondition

(P)ConjcondUion

4.8.27. Conjcontjajy fact

4.8.30.

(P)Conjcontrary

fact

(E)Conj contrary

fact

se; (E)Conjcontrary (P)Conjfalse (E)Conjfalse

man

(P)Conjfalse

fact

man

->

although

"

mesmo que; " J(P)Conjcondition |(E)Conjcondition

4.8.29. Conjfalae

unless

(P) Conj c 0 n c e s s i 0 u (E)Conjconce33ion

4.8.20. (P)Conjconcession

(P)Conjcontrary

in case

a menos que; (E)Conjpr0Vi80j

4.8.19. Coil j concession

4.8.28.

provided that

fact

if

—• if

man man

como se; (E)Conjfalse

man

— as if

Let x and x ' = any other elements in S; let y and y' = any other elements in S'. 4.8.31. x + To + x ' + Conj condition + y + Te' + y' Pres Fut Past

x ' + Conj condition + y

Pres (P)Fut 9bic (E)Pres Past

86

TWO-STRING GRAMMATICAL

4.8.32. x + Te + x ' + Conj coatrary

fact + sbjc

TRANSFORMATIONS

y + Te' + y '

(P)Past y ' -f x (E)Past J 1 x ' + Conj contrary fact + y

Conj contrary f a o t + y

(P)Past f u t (E)Past f u t 4.8.33. S + Conj false m a n + y + Te + y ' S + ConjfalBe man + y (P)Past 8bic (E)Past x

(P)Past f u t (E)Past f u t (P)Past 8bjc (E)Past

Let x and its primes = any other elements. 4.8.34. x rConjcontrary fact x ' + (E)V: was + x " L C o n j false man x ' -f were + x " x |Conj contrary LConjfalse

fact man

Let x and x ' = any other elements in S; let y and y ' = any other elements in S'; let z = subscripts purpose, neg result, or proviso. 4.8.35. x + Te + x ' + (P)Conj z + y + Te' + y' (P)Pres (P)Fut (P)Past

x ' + (P)Conj z + y

-

(P)Pres 8bic ] y ' (P)Past 8bj0 J

Let x and x ' = any other elements in S; let y and y ' = any other elements in S'. 4.8.36. x + Te + x ' + (E)Conj purp0S0 + y + -[Te' + Per] ((E)V modi ) y ' ^ (P)Pres x ' + (E)Conj, (E)Fut (E)Past _

4.8.37.

x

+ y

can be able to will be able to can will be able to could

+ Te + x ' + ConjconceBsion + y + -[Te' + Per]

(P)V modl l y' (E)V mod J + y + -[Te' + Per]

Pres ] ] x ' + Conj concession Fut Past (P)Pres 8bi ° + Per + (P)V mod may (P)Past 8bic + Per + ()PV modi might

TWO-STRING GRAMMATICAL TRANSFORMATIONS

Let x and x ' = any other elements. 4.8.38. x + without + Subj: [ N P PN

] + -[Te + Per] ((E)V mc + Mkr inf ) x '

(E)ProNomsubj

x 4- without

N P -8 PN (E)Poss

-[-inp] ((E)V modi : be- able to) x '

4.8.39. x + without + x ' + -[-ircy] + (E)V p i o g + Mkr p r p + x " x + without 4- x ' + -[-iwgr] + x "

4.9. COORDINATION

4.9.1.

S =• S' S": S

4.9.2.

S

Compnd Altern Conj m o t Altern bis Conj if . then Conj extran

Altern bis Conjjf.t^ Altern», ^bis, Conj if

S'

S' Altern,bis, Conj tben

S'

(P)Conj mot (E)Conj mot

4.9.3.

Conj mot

4.9.4.

(P)Conj mot

porque; (E)Conj mot —• because

4.9.5.

Altern bi9i

J(P) Altern j(E)Altern biSi

4.9.6.

Altern bi6j

4.9.7.

(E)Altern biSi

4.9.8.

Conj if

4.9.9.

(P)Conj if

4.9.10. Conj then

i(P)Altern ((E) Altern either f(P)Conj if |(E)Conj i f se; (E)Conj if —>- if i(P)Conj theil l(E)Conj t h e n

88 4.9.11.

TWO-STRING GRAMMATICAL

(P)Conj then

TRANSFORMATIONS

entao; (E)Conj then ->• then (P)Conj extran (E)Conj cxtrau

4.9.12. Conj 0xtran 4.9.13.

(P)Conj extran

(P)Conj extrani , (P)Conj extran> , (P)Conj e x t r a n s . . .

4.9.14.

(E)Conj extran

(E)Conj extrani , (E)Conj extran> , (E)Conj cxtrans . . .

4.9.15.

(P)Conj extrani

mas; (E)Conj extrani — but

4.9.16.

(P)Conj cxtrans

porem; (E)Conj extraBi — however

4.9.17.

(P)Conj extrani

ainda que; (E)ConjexfcraDi ->• although

Let x and its primes — any other elements in S; let y and its primes — any other elements in S'; let z = any other subscripts. 4.9.18. Portuguese and English Optional Transformation Subj (x) N e g l Subj: ProNom,indef/z/neg DP:[Indef z / n e g + x] + x ' LNeg2 + Subj + Neg 3 + Subj' S u b j " (y) N e g l S u b j " : rProNom fndef/z/neg DP: [Indef z/neg + y] + y ' Neg 2 + S u b j " + Negj + S u b j ' " J Subj: (x) Negj Subj: P r o N o r r i :indcf/z/neg DP: [Indef z/neg + x] + x ' |_Ncga + Subj + Neg 3 + Subj'_ (y) S u b j " S u b j " : ProNom indcf/z/ncg DP: [Indef z/ncg + y] + y ' S u b j " + Altern + S u b j ' " 4.9.19. x -f Neg s + S u b j " : x + Neg 3 + S u b j " :

Compnd

x " + Neg 3

y'

(P)ProNom indef/z/neg DP: [(E)Indef z/ucg + y] + y '

y

—

(E)ProNom indef/z/neg , [DP: [(E)Indef z/neg . + y] + y '

4.9.20. Portuguese and English Optional Transformation Subj (x) N e g l + x ' + Neg 3 (y) Subj' + y ' Neg, (x) Subj + x ' + Neg 3 (y) Subj' + y ' Let x and its primes = any other element or null; let y = any subscripts except mod, perf, prog (unless Te •= Fut), or aux; (E)V y = be- and (E)V.pass when Te = Pres or Past.

TWO-STRING GRAMMATICAL

TRANSFORMATIONS

89

4.9.21. x + Neg 3 + Subj + x ' + -[Te + Per] x' be- 4- able to can E)V perf (E)V prog (E)V aux (E)V y x + Neg 3 + -[Te + Per] be- + Subj x ' + able to can + Subj + x ' E)V perf + Subj + x' (E)V prog + Subj + x ' (E)V aux + Subj + x' (E)V y + Subj + x ' Let x include Neg 3 and any other elements. 4.9.22. x + be- + going to -f be- -f Subj + x ' -f able to + x " -> x + 6e- + Subj 4- x ' 4- going to + be able to 4- x " Let x = other elements and x ' and x " the same elements as in rule 4.9.21; let y = any subscript except mod. 4.9.23. x + 6e- 4- going to + Subj + x ' + x " ->• x 4" be- 4" Subj 4- x ' 4- going to 4- x " Let Subj = Subj'; let x and x ' = any other elements or null in S; let y and y ' = any other elements or null in S'. 4.9.24. Portuguese and English Optional Transformation x 4- Subj 4-

x

'

Compnd Altern

x + Subj 4- x '

Compnd Altern

y 4- Subj' 4- y ' y + y'

Let x and its primes = any other elements or null in S; let y and y ' = any other elements or null in S'; x — y, x ' = y', Te = Te', Subj Subj'. 4.9.25. Portuguese and English Optional Transformation x 4- Subj + -[Te + Per] 4- x ' + Compnd 4- y 4- Subj' -f -[Te ' + P e r ' ] + y ' ^ x + Subj 4- Compnd + Subj' 4- -[Te 4- Per -f P e r ' ] 4- x '

90

TWO-STRING GRAMMATICAL

TRANSFORMATIONS

4.9.26. Portuguese and English Optional Transformation x + Subj + -[Te + Per] -f x '

x + Subj

4.9.27.

x

(P)Altern (E)Altern (P)Neg 2 (E)Neg 3

(P)Altern (E)Altern (P)Neg 2 (E)Neg 3

y + Subj' + -[Te' Per'] + y' -

Subj' + -[l'è + Per + P e r ' ] + x '

(E)Compnd (E) Alter (E)Negj

x ' + -[Te + Per + P e r ' ] + x "

(E)Compnd (E)Altern (E)Negj

-[Te + (E)Not 3p 8g ] -[Te + (E)Per']

Let x and x ' — any other elements; let y = any subscript or combination of subscripts. 4.9.28.

x

lp y Per 2p y Per 3p y

+ + + + +

Per' lpy Per' 2p y 3p y

IPpi Pp'y L3ppl J 2

Rules 2.441 and 2.442 must be applied if the string includes ProNom r e f l . Let m Subj = N P containing m N, m PN, or ProNom with subscript m; let f Subj = N P containing f N, f PN, or ProNom with subscript f; let Y = (P) Compnd, (P)Altern, or (P)Neg 2 ; let x and its primes = any other elements or null. 4.9.29. Xx " m Subj " Y "Subj' " x ' + (P)VDa88 + -(P)Mkr f m Subj Subj' /Subj /Subj' . X

" m Subj " Y 'Subj' " x ' + (P)V pass m f Subj ' Subj f f Subj Subj'

-dos -dos

TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.10. REDUCED SENTENCES

Let x and x ' = any other elements. 4.10.1. x + Conj:

Subj + -[Te -f Per] + x ' antes depots sem para apesar de a firn de

que

porque quando (Subj)-[(P)Mkr i n f (Per)] + x ' antes de depois de sem para apesar de a firn de por ao 4.10.2. If Portuguese string = (Subj: antes de depois de sem para apesar de a firn de por ao

NP ) -[(P)Mkr lnf (Per)] + x ' PN ProNom subj _

91

92

TWO-STRING GRAMMATICAL TRANSFORMATIONS

Then English string -— (Subj:

x before after without for the purpose of in spite of for the purpose of

-t(E)Mkr p r p ] + x '

NP - s PN (E)Poss _

because of upon 4.10.3.

English Optional Transformation x + for the purpose of -f- -[(E)Mkr p r p ] -f- x' x + in order to -f x '

4.10.4.

x -)- in order to -f can + x ' —>x -f in order to -f- be able to -fx'

4.10.5.

x + (P)Mkr i n

2

PSBI 3Ppi

-re-r-

L 2Ppi,

2

P 8Bl 3p pl -des

4.11. COMPARISON OF EQUALITY A N D INEQUALITY

L e t X and Y ----- noun, adjective, or adverb compared; let x and its primes = a n y other elements or null in S; let y a n d its primes — a n y other elements or null in S'; x ' and y ' — D P elements if X and Y are nouns; x ' a n d y ' = Adv d e g if X and Y are adverbs or adjectives. 4.11.1.

S: x + [ ( x ' ) X ] + x " =• S': y + [ ( y ' ) Y ] + y " S " : x + Compar + X + x " + Conj c o m p a r + y + Y +

y"

L e t x ' = y ' excopt t h a t contained P e r in x ' -- or ^ contained P e r ' in y ' . Comparison of Subjects in Identical E n v i r o n m e n t 4.11.2.

Subj: [Compar + X ] + x ' + Conj c o m p a r + S u b j ' : [Y] + y '

-

Subj: [Compar + X ] + x ' + Conj c o m p a r + S u b j ' : [Y] (y') Subj: [Compar + X ] + Conj c o m p a r + S u b j ' : [Y] + y ' L e t X a n d Y — identical compared elements; let x a n d x ' = a n y elements or null in S; let y and y ' = a n y elements or null in S'.

TWO-STRING GRAMMATICAL

TRANSFORMATIONS

93

Comparison of Identical Elements 4.11.3. x + Compar + X + x ' + Conj compar + y + Y + y ' x + Compar + X + x ' + Conj compar + v + y ' Let x and x ' = any elements or null in S; let y and its primes = any element or null in S'; let z = any subscript except mod, perf, prog, or pass. Comparison of Indentical Elements in Identical Environment 4.11.4. English Optional Transformation Sub] + x + Compar -f X + x ' + Conj comrar + Subj' -[Te + Per] (y) V z + y ' (Prep) Y + y " .-[Te + Per] + V, + y + Y + y ' Subj + Compar + X + x ' + Conj compar + Subj' "0 -[Te + Per] (y) V, + y ' (Prep) .-[Te + Per] + V aux 4.11.5. Portuguese Obligatory Transformation Subj + x -f Compar + X + x ' + Conj compar -f Subj' -f y Subj + x + Compar + X + x ' + Conj compar + Subj' 4.11.6. x -f Compar -f X + x ' + Conj compar + y ^ Compar eqnnl Compar ine(|Ual 4.11.7.

Compar equal

4.11.8.

Compar equal(ipg

4.11.9.

Compar e a u a l i m t

4.11.10. Compar Inequal 4.11.11. Compar nl0rc 4.11.12. Compar lcss 4.11.13. (P)Compar eq „ alomt

x

Conjcompnr

cqual

Conj compar infciual Compar equaldeg Compar equaIamt i(P)Compar equa , deg j(E)Comparequaldcg j(P)Compar equalamt [(E)Compar equalamt Compar more Comparle3S j(P)Compar more |(E)Compar morc |(P)Compar IC8S |(E)Compar less i(P)Com P ar equalamtsB (P)Compar equalam

y

94

TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.11.14. íP)Compar e q u a l

t amV

-

Compar^,^ l C«-ParequalamW f

'-Compar e q ü a l a m t p |

4.11.15. (P)Compar e q u a l a m t p i ,-cnn 4.11.16. (E)Compar

r

Compar e q u a l a m t p |

í(E)

qual

C o m

a r

P

e q u a amt lam

«st

|(E)Compar e q u a l a m t p i 4.11.17.

x + Compar e q u a l + X + x ' — X : "ÍAdv Compar equa , deg Adj Cornpar e q u a l a m t

Let z = nuil or subscript c. 4.11.18. x + (P)Cornpar equalamt + N + x ' x

" m Cornpar e(lualamt8g f

f

N, V J

Compar e q u a l a m t p i

4.11.19. x + X

Nu z

" m Cornpar equalamtpi f

f

.

Compar e u q a l a m t s g

;

+ N + x'

"(E)Compar e q u a W t 9 B " (E)Compar e q u a l a m t p ]

"(E)N n (

E

m

Cornpar e q u a l a m t ¡ 8g 4.11.23. f Compar e q u a l a m tlj 9g

m

4.11.25.

f

Cornpar e q u a l a m t p i

Compar e q u a l a m t p i

4.11.26. (P)Conj c o m p a r equal 4.11.27. Conj c o m p a r

N

v

inequal

. qual

qual

4.11.21. (P)Compar equa , deg

4.11.24.

)

(P)Conj c 0 m p a r (E)Conj c o m p a r

4.11.20. Conj c o m p a r equal

4.11.22.

pi

tao; (E)Compar e q u a l d e g ^ as tanto; (E)Compar e q u a l a r a t

—> as much

tanta; " tantos; (E)Compar e q u a , a m t ^ -»• as many tantas; " comojquanto; (E)Conj c o m p a r (P)Conj c o m p a r nequal (E)Conj c o j n p a r nequal

as equal

95

TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.11.28.

(P)Compar more

mais;

inequa i

more

menos; (E)Compar less -»• less

4.11.29. (P)Compar lesa 4.11.30. (P)Conj c0mpar

(E)Compar more

—

do que; (E)Conj compar

inequal

than

4.12. SUPERLATIVE COMPARISON

Let x and x ' = any other elements or null in S; let y and y ' = any other elements or null in S'; let x ' = y'. Comparison of Comp: (Adv deg ) Adj 4.12.1. S: x + Comp: [(Adv deg )Adj] + x ' = • S': y + Comp: [(Adv deg )Adj] + y ' S " : x + Comp: [Def + Compar inequal + Adj] + x ' Let x and its primes = any other elements. 4.12.2. x + ( P ) D e f + x '

m

Adj s g Adj 9g m Adj p l . f Ad] pl j f

m

Def sg Def sg m Adj p l ' f Adj p l f

m

Adj ag Adj s g m Adj p l f Adj p l j f

4.12.3. x + estd- + x ' + Compar inequa , + x " x -f se- + x ' + Compar inequa , + x " Let x and x ' = any other elements or null in S; let y and y ' = any other elements or null in S'; let x ' = y ' ; let X = element compared in S; let Y = element compared in S'; X and Y do not include (Adv deg ). Comparison of Tm, Loc, or Man: Adv 4.12.4. S: x + X + x ' S': y + Y + y ' S " : x + Def + Compar inequal + X + x ' Let x and its primes = any other elements. 4.12.5. x + (P)Def + x ' + X + x " ^ x + m Def s „ + x ' + X + x " 4.12.6. x + Def more less

x -f Def

most least

96

TWO-STRING GRAMMATICAL TRANSFORMATIONS

4.13. PORTUGUESE OBLIGATORY TRANSFORMATION

Let x and its primes — any other elements or null. X' + Aux + (P)V tr + x " (P)ProNom dn 4.13.1. x (P)Neg (P)Rel (P)ProNom io (P)Conj L(P)ProNom refl J ,(P)Q (P)Neg (P)Rel (P)Conj

Aux + (P)V tr + x " + x ' (P)ProNom d o (P)ProNom io (P)ProNom refl J

(P)Q 4.14. PORTUGUESE OPTIONAL TRANSFORMATION

Let x and its primes = any other elements or null, except that x 4.14.1. x + Aux + (P)V tr + x ' (P)ProNom d o (P)ProNom 10 (P)ProNom r e f l J (P)ProNom d 0 (P)ProNom io (P)ProNom refl

Aux + (P)V t , + x ' + x "

null.

V COMPUTER OUTPUT FROM PROGRAMMED

GRAMMAR

The following pages contain computer output the result of running for f i f t y iterations a program automating the grammar preceding this chapter. Details about the computer program a n d this o u t p u t appear in the first chapter. ITERATION

1

KERNEL POR. OS ESTADOS UNIDOS PODER(AO ESTAR LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS . ONE STRING TRANSFORMATIONS COMPOUND POR. OS ESTADOS UNIDOS PODERIAO ESTAR QUENTES E LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE HOT AND CLEAN FOR EVERY TWENTY DAYS . ALTERNATE POR. OS ESTADOS UNIDOS PODER(AO ESTAR QUENTES OU LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE HOT OR CLEAN FOR EVERY TWENTY DAYS . NEGATIVE POR. OS ESTADOS UNIDOS N(AO PODER(AO ESTAR QUENTES NEM LIMP OS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL NOT BE ABLE TO BE HOT NOR CLEAN

98

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

FOR EVERY

TWENTY DAYS .

INTERROGATIVE POR. ONDE OS ESTADOS UNIDOS N(AO PODER(AO ESTAR QUENTES NEM LIMPOS POR CADA VINTE DIAS .Q ENG. WHERE WILL THE UNITED STATES NOT BE ABLE TO BE HOT NOR CLEAN FOR EVERY TWENTY DAYS .Q TWO STRING

TRANSFORMATIONS

MODIFICATION KERNEL POR. OS ESTADOS UNIDOS PODERt AO ESTAR LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS . DERIVED KERNEL POR. MUDA OS ESTADOS UNIDOS . ENG. IT CHANGES THE UNITED STATES . TRANSFORMED KERNEL POR. OS ESTADOS UNIDOS QUE MUDA PODERiAO ESTAR LIMPOS POR C ADA VINTE DIAS . ENG. THE UNITED STATES THAT IT CHANGES WILL BE ABLE TO BE C LEAN FOR EVERY TWENTY DAYS . SENTENCE AS NOUN PHRASE KERNEL POR. OS ESTADOS UNIDOS PODERIAO ESTAR LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS . KERNEL CONTAINING NOUN PHRASE POR. A VERDADE E) ISTO. ENG. THE TRUTH IS THIS.

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

99

TRANSFORMED KERNEL POR. A VERDADE E) QUE OS ESTADOS UNIDOS PODERtAO ESTAR LIMP OS POR CADA VINTE DIAS . ENG. THE TRUTH IS THAT THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY TWENTY DAYS . SENTENCE AS TIME ELEMENT KERNEL POR. OS ESTADOS UNIDOS PODERtAO ESTAR LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS . KERNEL CONTAINING TIME ELEMENT POR. MARIA ESTARA) AQUI AMANHIA* ENG. MARY WILL BE HERE'TOMORROW. TRANSFORMED KERNEL POR. MARIA ESTARA) AQUI QUANDO OS ESTADOS UNIDOS PUDEREM ES TAR LIMPOS POR CADA VINTE DIAS . ENG. MARY WILL BE HERE WHEN THE UNITED STATES ARE ABLE TO B E CLEAN FOR EVERY TWENTY DAYS . SENTENCE AS PLACE ELEMENT KERNEL POR. OS ESTADOS UNIDOS PODERtAO ESTAR LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS . KERNEL CONTAINING PLACE ELEMENT POR. JO(AO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JOIAO COME ONDE OS ESTADOS UNIDOS PODER(AO ESTAR LIMPO

100

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

S PGR CADA VINTE DIAS . ENG. JOHN EATS WHERE THE UNITED STATES WILL BE ABLE TO BE C LEAN FOR EVERY TWENTY DAYS . SENTENCE AS MANNER ELEMENT KERNEL POR. OS ESTADOS UNIDOS PODER(AO ESTAR LIMPOS POR CAOA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS . DERIVED KERNEL POR. OS PANOS PODEM ESTAR LIMPOS . ENG. THE CLOTHS ARE ABLE TO BE CLEAN . TRANSFORMED KERNEL POR. OS ESTADOS UNIDOS PODERIAO ESTAR LIMPOS POR CADA VINTE DIAS COMO OS PANOS PODEM ESTAR LIMPOS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS LIKE THE CLOTHS ARE ABLE TO BE CLEAN . OPTIONAL ONE STRING TRANSFORMATION POR. OS EST ADOS UNIDOS P O D E R U O ESTAR LIMPOS POR CADA VINTE DIAS COMO OS PANOS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS LIKE THE ClOTHS . INTERROGATIVE AS NOUN PHASE INTERROGATIVE POR. ONDE OS ESTADOS UNIDOS N(AO PODER(AO ESTAR QUENTES NEM LIMPOS POR CADA VINTE DIAS .Q ENG. WHERE WILL THE UNITED STATES NOT BE ABLE TO BE HOT NOR CLEAN FOR EVERY TWENTY DAYS -Q KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH.

COMPUTER OUTPUT FROM PROGRAMMED RGAMMAR TRANSFORMED

101

KERNEL

POR.

EU S E I ONDE OS T E S NEM L I M P O S

ESTADOS UNIDOS POR CAOA V I N T E

ENG.

I KNOW WHERE T H E U N I T E D HOT NOR C L E A N FOR E V E R Y

STATES TWENTY

N(AO DIAS

PODERtAO .

WILL DAYS

NOT .

BE

ESTAR

ABLE

QUEN

TO

BE

CONCESSION KERNEL POR.

OS ESTADOS DIAS .

UNIDOS

PODERIAO

ENG.

THE UNITED WENTY DAYS

STATES .

WILL

ADDITIONAL POR.

E=LE

ENG.

HE

ESTAR

BE A B L E

LIMPOS

TO BE

POR

CLEAN

CADA

FOR

VINTE

EVERY

T

KERNEL

TRABALHARA).

WILL

WORK.

TRANSFORMED

KERNEL

POR.

E = L E T R A B A L H A R A ) MESMO QUE OS ESTADOS TAR L I M P O S POR CADA V I N T E D I A S .

ENG.

HE W I L L WORK E V E N THOUGH THE U N I T E D BE C L E A N FOR E V E R Y TWENTY DAYS .

UNIDOS

STATES

POSSAM

ARE

ABLE

ES

TO

CONDITION KERNEL POR.

OS ESTADOS DIAS .

UNIDOS

PODERUO

ENG.

THE U N I T E D WENTY DAYS

STATES .

WILL

ADDITIONAL POR.

E=LE

ENG.

HE W I L L

BE A B L E

TO

LIMPOS

BE

POR

CLEAN

CADA

VINTE

FOR

EVERY

T

POSSAM

ESTAR

L

KERNEL

TRABALHARA). WORK.

TRANSFORMED POR.

ESTAR

E=LE T R A B A L H A R A )

KERNEL CASO

OS

ESTADOS

UNIDOS

102

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

IMPOS POR CADA V I N T E ENG.

DIAS

.

HE W I L L WORK I N C A S E THE U N I T E D S T A T E S C L E A N FOR E V E R Y TWENTY DAYS .

ARE A B L E

TO BE

PURPOSE KERNEL POR.

OS ESTADOS UNIDOS DIAS .

PODER(AO

ENG.

THE U N I T E D S T A T E S WENTY DAYS .

WILL

ADDITIONAL POR.

E=LE

ESTAR L I M P O S

POR CADA

VINTE

BE A B L E TO BE C L E A N FOR E V E R Y

T

KERNEL

TRABALHARA).

E N G . HE W I L L

WORK.

TRANSFORMED

KERNEL

POR.

E=LE T R A B A L H A R A ) PARA QUE OS ESTADOS UNIDOS POSSAM AR LIMPOS POR CADA V I N T E D I A S .

ENG.

HE W I L L WORK I N ORDER THAT THE U N I T E D EAN FOR EVERY TWENTY DAYS . NEGATIVE

EST

S T A T E S CAN BE

CL

RESULT

KERNEL POR.

OS ESTADOS UNIDOS DIAS .

E N G . THE U N I T E D S T A T E S WENTY DAYS . ADDITIONAL POR.

E=LE

ENG.

HE W I L L

PODER(AO WILL

ESTAR L I M P O S PDR CADA

VINTE

BE A B L E TO B E C L E A N FOR E V E R Y T

KERNEL

TRABALHARA). WORK.

TRANSFORMED

KERNEL

POR.

E=LE T R A B A L H A R A ) SEM QUE OS ESTADOS U N I D O S POSSAM R L I M P O S POR CADA V I N T E D I A S .

ENG.

HE W I L L WORK WITHOUT THE U N I T E D S T A T E S B E C L E A N FOR E V E R Y TWENTY DAYS .

ESTA

) B E I N G A B L E TO

103

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR PREDICTIVE

IF

KERNEL POR.

OS E S T A D O S DIAS .

UNIDOS

P O D E R ( A O .ESTAR

ENG.

THE U N I T E D WENTY D A Y S

STATES .

WILL

ADDITIONAL POR.

E=LE

ENG.

HE

BE

ABLE

LIMPOS

TO

BE

POR

CLEAN

CADA

FOR

VINTE

EVERY

T

KERNEL

TRABALHARA).

WILL

WORK.

TRANSFORMED

KERNEL

POR.

E=LE MP OS

T R A B A L H A R A ) SE POR CADA V I N T E

ENG.

HE W I L L WORK I F T H E U N I T E D FOR E V E R Y TWENTY DAYS . CONTRARY

OS E S T A D O S DIAS .

UNIDOS

STATES

ARE

PUDEREM

ESTAR

ABLE

BE

CLEAN

CADA

VINTE

TO

LI

IF

KERNEL POR.

OS E S T A D O S DIAS .

UNIDOS

PODER(AO

ENG.

THE U N I T E D WENTY D A Y S

STATES .

WILL

ADDITIONAL POR.

EU 0

ENG.

I

BE

ESTAR

ABLE

LIMPOS

TO

BE

POR

CLEAN

FOR

EVERY

T

KERNEL

FAREI.

WILL

DO

IT.

TRANSFORMED

KERNEL

POR.

EU 0 F A R I A SE OS E S T A D O S POR C A D A V I N T E D I A S .

ENG.

I WOULD DO I T I F THE U N I T E D AN F O R E V E R Y T W E N T Y D A Y S . FALSE KERNEL

MANNER

UNIDOS

PUDESSEM

STATES

WERE

ESTAR

ABLE

TO

LIMPOS

BE

CLE

104

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR.

OS E S T A O O S DIAS .

UNIDOS

POOERiAO

ENG.

THE UNITEO WENTY DAYS

STATES .

WILL

ADDITIONAL POR.

E=LE

ENG.

HE W I L L

BE

ESTAR

ABLE

TO

LIMPOS

POR CADA

BE C L E A N

FOR

VINTE

EVERY

T

KERNEL

TRABALHARA). WORK.

TRANSFORMED

KERNEL

POR.

E = L E T R A B A L H A R A ) COMO SE OS E S T A D O S TAR L I M P O S POR CADA V I N T E D I A S .

ENG.

HE W I L L WORK AS I F T H E U N I T E D L E A N FOR E V E R Y TWENTY DAYS .

UNIDOS

STATES

PUDESSEM

WERE A B L E ' T O

ES

BE

C

COMPARATIVE KERNEL POR.

OS E S T A D O S DIAS .

UNIDOS

PODER(AO

ENG.

THE U N I T E D WENTY DAYS

STATES .

WILL

DERIVED

BE

ESTAR

ABLE

TO

LIMPOS

POR

BE C L E A N

CADA

FOR

VINTE

EVERY

T

KERNEL

POR.

Q U A S E TODOS OS T A R L I M P O S POR

SEUS PR01XIM0S V I N T E CADA V I N T E DIAS .

ENG.

ALMOST A L L TWENTY E C L E A N FOR E V E R Y TRANSFORMED

PRATOS

OF HER N E X T D I S H E S TWENTY D A Y S .

WILL

PODERIAO

BE A B L E

ES

TO

B

KERNEL

POR.

OS E S T A D O S V I N T E DIAS PRATOS .

UNIDOS DO QUE

ENG.

THE ERY HES

S T A T E S WILL BE A B L E T O B E MORE C L E A N FOR EV DAYS T H A N ALMOST A L L TWENTY OF HER N E X T D I S

UNITEO TWENTY .

SUPERLATIVE KERNEL

PODERIAO ESTAR Q U A S E TODOS OS

MAIS SEUS

L I M P O S POR C A D A PRO)X*MOS V I N T E

COMPUTER OUTPUT FROM PliOURAMMED GRAMMAR

105

POR. OS ESTADOS UNIDOS P00ERCA0 ESTAR LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES WILL BE ABLE TO BE CLEAN FOR EVERY T WENTY DAYS . DERIVED KERNEL POR. OUTROS ELEMENTOS P0DER(A0 ESTAR LIMPOS POR CADA VINTE DIAS . ENG. OTHER ELEMENTS WILL BE ABLE TO BE CLEAN FOR EVERY TWEN TY DAYS . TRANSFORMED KERNEL POR. OS ESTADOS UNIDOS PODERJAO SER OS MAIS LIMPOS POR CADA VINTE DIAS . ENG. THE UNITED STATES HILL BE ABLE TO BE THE CLEANEST FOR EVERY TWENTY DAYS .

ITERATION

2 KERNEL

POR. DUAS ARRAS SUSTENTAM UMA VERDADE . ENG. TWO BANNS SUPPORT ONE TRUTH . ONE STRING TRANSFORMATIONS PASSIVE POR. UMA VERDADE E) SUSTENTADA POR DUAS ARRAS . ENG. ONE TRUTH IS SUPPORTED BY TWO BANNS . COMPOUND POR. UMA VERDADE E QUASE TODOS OS OUTROS AMORES SIAO SUSTEN TADOS POR DUAS ARRAS . ENG. ONE TRUTH AND ALMOST ALL THE OTHER LOVES ARE SUPPORTED BY TWO BANNS . ALTERNATE

106

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR,

POR.

UMA VERDADE OU QUASE TODOS NTADOS POR DUAS ARRAS .

ENG.

ONE TRUTH OR ALMOST BY TWO BANNS .

ALL

OS OUTROS

THE

OTHER

AMORES

LOVES

S(AO

ARE

SUSTE

SUPPORTED

NEGATIVE POR.

NEM UMA V E R D A D E SUSTENTADOS POR

ENG.

N E I T H E R ONE U P P O R T E D BY

NEM QUASE TODOS DUAS ARRAS .

T R U T H NOR TWO BANNS

ALMOST .

ALL

OS OUTROS

THE

AMORES

OTHER

S < AO

LOVES

ARE

S

INTERROGATIVE POR.

POR QUE ES S ( A O

NEM UMA VERDADE S U S T E N T A D O S POR

ENG.

WHY ARE N E I T H E R ES S U P P O R T E D BY

NEM QUASE TODOS DUAS ARRAS . Q

ONE TRUTH TWO BANNS

TWO S T R I N G

NOR .Q

ALMOST

ALL

OS OUTROS

THE

OTHER

TRANSFORMATIONS

MODIFICATION KERNEL POR.

DUAS

ARRAS

ENG.

TWO BANNS

SUSTENTAM SUPPORT

DERIVED POR.

DUAS

ARRAS

ENG.

TWO BANNS

DUAS

ARRAS

ENG.

TWO BANNS

ONE

TRUTH

DETERIORAM

.

DETERIORATE

.

QUE WHICH

SENTENCE

VERDADE

.

.

KERNEL

TRANSFORMED POR.

UMA

KERNEL

DETERIORAM

SUSTENTAM

DETERIORATE AS

NOUN

SUPPORT

PHRASE

KERNEL POR.

DUAS

ARRAS

ENG.

TWO 8ANNS

SUSTENTAM SUPPORT

ONE

UMA

VERDADE

TRUTH

.

.

UMA

VERDADE

ONE

TRUTH

.

.

AMOR

LOV

107

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR KERNEL POR.

E)

POSSDVEL

ENG.

IT

IS

CONTAINING

NOUN

PHRASE

POSSIBLE. TRANSFORMED

POR.

E)

POSSDVEL

ENG.

IT

IS

QUE

KERNEL OUAS

POSSIBLE

THAT

SENTENCE

AS

ARRAS

SUSTENTEM

TWO BANNS

TIME

SUPPORT

UMA

VERDADE

ONE

TRUTH

.

.

ELEMENT

KERNEL POR.

DUAS

ARRAS

ENG.

TWO BANNS

SUSTENTAM SUPPORT

KERNEL POR.

PAULO

ENG.

PAUL

ESTA) IS

PAULO DE .

ENG.

PAUL

ESTA)

IS

.

TIME

ELEMENT

DUAS

ARRAS

NOW.

AQUI

HERE

.

AGORA.

TRANSFORMED POR.

VERDADE

TRUTH

CONTAINING AQUI

HERE

ONE

UMA

QUANDO

WHEN

SENTENCE

KERNEL

AS

TWO BANNS PLACE

SUSTENTAM

SUPPORT

ONE

TRUTH

UMA

VERDA

.

ELEMENT

KERNEL POR.

DUAS

ARRAS

ENG.

TWO BANNS

SUSTENTAM SUPPORT

KERNEL POR.

JO(AC

ENG.

JOHN

COME EATS

JO(AO

COME

VERDADE

TRUTH

CONTAINING

.

.

PLACE

ELEMENT

AQUI . HERE.

TRANSFORMED POR.

ONE

UMA

ONDE

KERNEL

DUAS

ARRAS

SUSTENTAM

UMA

VERDADE

.

108 ENG.

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR JOHN

EATS

WHERE

SENTENCE

TWO BANNS AS

MANNER

SUPPORT

ONE

TRUTH

.

ELEMENT

KERNEL POR.

OUAS

ARRAS

ENG.

TWO BANNS

SUSTENTAM SUPPORT

DERIVED POR.

AS

CDISAS

ENG.

THE

ONE

SUPPORT

TRANSFORMED

ONE

ENG.

TWO BANNS E TRUTH .

SUPPORT

OPTIONAL ARRAS

ENG.

TWO BANNS

.

.

UMA

ONE

ONE

.

VERDADE

TRUTH

STRING

SUSTENTAM SUPPORT

TRUTH

.

KERNEL

DUAS ARRAS S U S T E N T A M TAM UMA VERDADE .

DUAS

TRUTH

UMA VERDADE

POR.

POR.

VERDADE

KERNEL

SUSTENTAM

THINGS

UMA

UMA

ONE

INTERROGATIVE

THE

COISAS

THINGS

SUSTEN

SUPPORT

ON

TRANSFORMATION VERDADE

TRUTH

AS

LIKE

COMO AS

NOUN

LIKE

COMO THE

AS

COISAS

THINGS

.

.

PHASE

INTERROGATIVE POR.

POR QUE ES S ( A O

NEM UMA V E R D A D E S U S T E N T A D O S POR

ENG.

WHY ARE N E I T H E R ES S U P P O R T E D BY KERNEL

POR.

EU

ENG.

I

SEI KNCW

A

NEM QUASE TODOS OS DUAS ARRAS . Q

ONE T R U T H TWO BANNS

CONTAINING

NOR .Q

NOUN

ALMOST

ALL

OUTROS

THE

OTHER

AMOR

LOV

PHRASE

VERDADE.

THE

TRUTH.

TRANSFORMED POR.

EU S E I POR QUE OS AMORES S ( A O

ENG.

I

KERNEL

NEM UMA VERDADE SUSTENTADOS POR

KNOW WHY N E I T H E R

ONE

TRUTH

NEM QUASE TODOS DUAS ARRAS .

NOR

ALMOST

ALL

THE

OS

OUTR

OTHER

109

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR LOVES

ARE

SUPPORTED

BY

TWO BANNS

.

CONCESSION KERNEL POR.

DUAS

ARRAS

ENG.

TWO BANNS

SUSTENTAM SUPPORT

ADDITIONAL POR.

E=LE

ENG.

HE

UMA

ONE

VERDADE

TRUTH

.

.

KERNEL

TRABALHA.

WORKS. TRANSFORMED

POR.

E^LE DE .

ENG.

HE

TRABALHA

WORKS E V E N

KERNEL

MESMO

QUE

THOUGH

DUAS

ARRAS

TWO BANNS

SUSTENTEM

SUPPORT

ONE

UMA

TRUTH

VERDA

.

CONDITION KERNEL POR.

DUAS

ARRAS

ENG.

TWO BANNS

SUSTENTAM SUPPORT

ADDITIONAL POR.

E=LE

ENG.

HE

ONE

UMA

VERDADE

TRUTH

.

.

KERNEL

TRABALHA.

WORKS. TRANSFORMED

POR.

E=LE

ENG.

HE

TRABALHA

WORKS

IN

KERNEL

CASO

CASE

DUAS

ARRAS

TWO BANNS

SUSTENTEM

SUPPORT

PURPOSE KERNEL POR.

DUAS

ARRAS

ENG.

TWO BANNS

SUSTENTAM SUPPORT

ADDITIONAL

ONE

UMA V E R D A D E TRUTH

KERNEL

.

.

ONE

UMA TRUTH

VERDADE .

.

110

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED

KERNEL

POR. E=LE TRABALHA PARA QUE DUAS ARRAS SUSTENTEM UMA VERDAD E . ENG. HE WORKS IN ORDER THAT TWO BANNS SUPPORT ONE TRUTH . NEGATIVE

RESULT

KERNEL POR. DUAS ARRAS SU5TENTAM UMA VERDADE . ENG. TWO BANNS SUPPORT ONE TRUTH . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED

KERNEL

POR. E=LE TRABALHA SEM QUE DUAS ARRAS SUSTENTEM UMA VERDADE ENG. HE WORKS WITHOUT TWO BANNS ) SUPPORTING ONE TRUTH . PREDICTIVE IF KERNEL POR. DUAS ARRAS SUSTENTAM UMA VERDADE . ENG. TWO BANNS SUPPORT ONE TRUTH . ADDITIONAL POR. E=LE

KERNEL

TRABALHARA).

ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA)

SE DUAS ARRAS SUSTENTAREM UMA VERDADE

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

111

ENG. HE WILL WORK IF TWO BANNS SUPPORT ONE TRUTH . CONTRARY

IF

KERNEL POR. DUAS ARRAS SUSTENTAM UMA VERDADE . ENG. TWO BANNS SUPPORT ONE TRUTH . ADDITIONAL

KERNEL

POR. EU 0 FAREI. ENG. I WILL DO IT. TRANSFORMED

KERNEL

POR. EU 0 FARIA SE DUAS ARRAS SUSTENTASSEM UMA VERDADE . ENG. I WOULD DO IT IF TWO BANNS SUPPORTED ONE TRUTH . FALSE MANNER KERNEL POR. DUAS ARRAS SUSTENTAM UMA VERDADE . ENG. TWO BANNS SUPPORT ONE TRUTH . ADDITIONAL

KERNEL

POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED

KERNEL

POR. E=LE TRABALHA COMO SE DUAS ARRAS SUSTENTASSEM UMA VERD ADE . ENG. HE WORKS AS IF TWO BANNS SUPPORTED ONE TRUTH .

ITERATION

3 KERNEL

POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN .

COMPUTER OTTTrUT FROM PROGRAMMED GRAMMAR

112

ONE

STRING

TRANSFORMATIONS

COMPOUND POR.

NO)S

ESTIVEMOS

ENG.

WE WERE

HOT

QUENTES

AND C L E A N

E LIMPOS

.

.

ALTERNATE POR.

NO)S

ESTIVEMOS

ENG.

WE WERE

QUENTES

HOT OR C L E A N

OU LIMPOS

.

.

NEGATIVE POR.

NO)S

N(AO

ENG.

WE WERE

ESTIVEMOS

NOT HOT

QUENTES

NOR C L E A N

NEM LIMPOS

.

.

INTERROGATIVE POR.

COMO NO)S

N(AO

ESTIVEMOS

ENG.

HOW WERE WE NOT

.Q

TWO S T R I N G SENTENCE

.0

TRANSFORMATIONS

AS

NOUN

PHRASE

KERNEL POR.

NO)S

ESTIVEMOS

ENG.

WE WERE C L E A N KERNEL

POR.

ERA

ENG.

IT

LIMPOS .

CONTAINING

ERA

ENG.

IT

NOUN

PHRASE

POSSDVEL. WAS

POSSIBLE. TRANSFORMED

POR.

.

POSSDVEL

QUE NOJS

WAS P O S S I B L E SENTENCE

KERNEL

FOR AS

US

TIME

E S T I V E ) SSEMOS TO BE C L E A N ELEMENT

.

LIMPOS

.

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . KERNEL CONTAINING TIME ELEMENT POR. EU ESTIVE LA) ONTEM. ENG. I WAS THERE YESTERDAY. TRANSFORMED KERNEL POR. EU ESTIVE LA) QUANDO NO)S ESTIVEMOS LIMPOS . ENG. I WAS THERE WHEN WE WERE CLEAN • SENTENCE AS PLACE ELEMENT KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . KERNEL CONTAINING PLACE ELEMENT POR. JO(AO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JOIAO COME ONDE NO)S ESTIVEMOS LIMPOS . ENG. JOHN EATS WHERE WE WERE CLEAN . SENTENCE AS MANNER ELEMENT1 KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . DERIVED- KERNEL POR. OS M O O O S EST1AO LIMPOS . ENG. THE LADS ARE CLEAN .

113

114

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

TRANSFORMED KERNEL POR. NO)S ESTIVEMOS LIMPOS COMO OS M O O O S EST(AO LIMPOS . ENG. WE WERE CLEAN LIKE THE LADS ARE CLEAN . OPTIONAL ONE STRING TRANSFORMATION POR. NO)S ESTIVEMOS LIMPOS COMO OS MOCJOS . ENG. WE WERE CLEAN LIKE THE LADS . INTERROGATIVE AS NOUN PHASE INTERROGATIVE POR. COMO NO)S N(AO ESTIVEMOS .0 ENG. HOW WERE WE NOT .Q KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI COMO NO)S N(AO ESTIVEMOS . ENG. I KNOW HOW WE WERE NOT . CONCESSION KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU MESMO QUE NO)S ESTIVE)SSEMOS LIMPOS . ENG. HE WORKED EVEN THOUGH WE WERE CLFAN .

COMPUTER OUTPUT FROM P R O G R A M M E D G R A M M A R

CONDITION KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . ADDITIONAL KERNEL POR. E-LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU CASO NO)S ESTIVE)SSEMOS LIMPOS . ENG. HE WORKED IN CASE WE WERE CLEAN . PURPOSE KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . ADDITIONAL KERNEL POR. E=LE TRABALHOUENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU PARA QUE NOJS ESTIVE)SSEMOS LIMPOS . ENG. HE WORKED IN ORDER THAT WE BE CLEAN . NEGATIVE RESULT KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . ADDITIONAL KERNEL POR. E=LE TRABALHOU.

115

116

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU SEN QUE NO)S>ESTIVE)SSEMOS LIMPOS . ENG. HE WORKED WITHOUT OUR BEING CLEAN . CONTRARY IF KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . ADDITIONAL KERNEL POR. EU IA FAZE=-LO. ENG. I WAS GOING TO DO IT. TRANSFORMED KERNEL POR. EU 0 TERIA FEITO SE NO)S TIVE)SSEMOS ESTADO LIMPOS . ENG. I WOULD HAVE DONE IT IF WE HAD BEEN CLEAN . FALSE MANNER KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU COMO SE NO)S ESTIVEJSSEMOS LIMPOS . ENG. HE WORKED AS IF WE WERE CLEAN . COMPARATIVE KERNEL

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

117

POR. NO)S ESTIVEMOS LIHPOS . ENG. WE WERE CLEAN . DERIVED KERNEL POR. E=STES PRO)XIMOS VINTE RAPAZES ESTIVERAM LIMPOS . ENG. THESE NEXT TWENTY BOYS WERE CLEAN . TRANSFORMED KERNEL POR. NO)S ESTIVEMOS MENOS LIHPOS DO QUE ENSTES PRO)XIMOS VI NTE RAPAZES . ENG. WE WERE LESS CLEAN THAN THESE NEXT TWENTY BOYS . SUPERLATIVE KERNEL POR. NO)S ESTIVEMOS LIMPOS . ENG. WE WERE CLEAN . DERIVED KERNEL POR. OUTROS SERES ESTIVERAM LIMPOS . ENG. OTHER BEINGS MERE CLEAN . TRANSFORMED KERNEL POR. NO)S FOMOS OS MAIS LIMPOS . ENG. WE WERE THE CLEANEST .

ITERATION

4 KERNEL

POR. MARIA A NOTA LA) CADA DOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . ONE STRING TRANSFORMATIONS PASSIVE

118

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR.

E)

NOTADA

ENG.

IT

IS

POR

MARIA

NOTICED

BY

LA)

MARY

CADA

THERE

OOIS EVERY

DIAS

BEM

.

TWO DAYS

WELL

.

COMPOUND POR.

E) NOTADA BEM .

POR M A R I A

ENG.

IT I S N O T I C E D N I G H T S WELL .

BY

LA)

MARY

CADA

THERE

DOIS DIAS

EVERY

E VINTE

TWO DAYS

NOITES

AND

TWENTY

E VINTE

NOITES

ALTERNATE POR.

E ) NOTADA POR M A R I A L A ) BEM OU F A f C I L M E N T E .

ENG.

I T I S N O T I C E D BY MARY NIGHTS WELL OR E A S I L Y

CADA

THERE .

DOIS

EVERY

DIAS

TWO DAYS

AND

TWENTY

NEGATIVE POR.

N(AO E) NOTADA N O I T E S BEM NEM

POR M A R I A L A ) CADA FA(CILMENTE .

ENG.

IT I S NOT N O T I C E D BY MARY T H E R E NTY N I G H T S WELL NOR E A S I L Y .

DOIS

EVERY

DIAS

NEM

VINTE

TWO DAYS NOR

TWE

INTERROGATIVE POR.

QUANDO N t A O E .Q

ENG.WHEN 0

IS

IT

E)

NOT

NOTADA

NOTICED

TWO S T R I N G SENTENCE

POR

BY

MARIA

MARY

LA)

BEM NEM

THERE

WELL

NOR

WELL

.

TRANSFORMATIONS

AS

NOUN

PHRASE

KERNEL POR.

MARIA

ENG.

MARY

A NOTA L A ) NOTICES KERNEL

POR.

A VERDADE E )

IT

CADA THERE

DOIS EVERY

CONTAINING ISTO.

DIAS

NOUN

BEM

TWO DAYS PHRASE

FAiCILMENT

.

EASILY

.

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

119

ENG. THE TRUTH IS THIS. TRANSFORMED KERNEL POR. A VERDADE E) QUE MARIA A NOTA LA) CADA OOIS DIAS BEM . ENG. THE TRUTH IS THAT MARY NOTICES IT THERE EVERY TWO DAYS WELL . SENTENCE AS TIME ELEMENT KERNEL POR. MARIA A NOTA LA) CADA DOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . KERNEL CONTAINING TIME ELEMENT POR. PAULO ESTA) AQUI AGORA. ENG. PAUL IS HERE NOW. TRANSFORMED KERNEL POR. PAULO ESTA) AQUI QUANDO MARIA A NOTA LA) CADA DOIS DIA S BEM . ENG. PAUL IS HERE WHEN MARY NOTICES IT THERE EVERY TWO DAYS WELL . SENTENCE AS PLACE ELEMENT KERNEL POR. MARIA A NOTA LA) CADA DOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . KERNEL CONTAINING PLACE ELEMENT POR. JO(AO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JO(AO COME ONDE MARIA A NOTA LA) CADA DOIS OIAS BEM . ENG. JOHN EATS WHERE MARY NOTICES IT THERE EVERY TWO DAYS W ELL .

120

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

SENTENCE AS MANNER ELEMENT KERNEL POR. MARIA A NOTA LA) CADA DOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . DERIVED KERNEL POR. A MO=C)A A ESTA) NOTANDO . ENG. THE GIRL IS NOTICING IT . TRANSFORMED KERNEL POR. MARIA A NOTA LA) CADA DOIS DIAS BEM COMO A MO=C)A A ES TA) NOTANDO . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL LIKE THE GIR L IS NOTICING IT . OPTIONAL ONE STRING TRANSFORMATION POR. MARIA A NOTA LA) CADA DOIS DIAS BEM COMO A MO=C)A . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL LIKE THE GIR L . INTERROGATIVE AS NOUN PHASE INTERROGATIVE POR. QUANDO NIAO E) NOTAOA POR MARIA LA) BEM NEM FA(CILMENT E .Q ENG. WHEN IS IT NOT NOTICED BY MARY THERE WELL NOR EASILY . 0 KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNCW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI QUANDO NIAO E) NOTADA POR MARIA LA) BEM NEM FA( CILMENTE . ENG. I KNOW WHEN IT IS NOT NOTICED BY MARY THERE WELL NOR E ASILY .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

121

CONCESSION KERNEL POR. MARIA A NOTA LA) CADA DOIS OIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . ADDITIONAL KERNEL POR. E*=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E*LE TRABALHA MESMO QUE MARIA A NOTE LAI CADA DOIS DlA S BEM . ENG. HE WORKS EVEN THOUGH MARY NOTICES IT THERE EVERY TWO 0 AYS WELL . CONDITION KERNEL POR. MARIA A NOTA LA) CADA OOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E=LE TRABALHA CASO MARIA A NOTE LA) CADA DOIS DIAS BEM ENG. HE WORKS IN CASE MARY NOTICES IT THERE EVERY TWO DAYS WELL . PURPOSE KERNEL POR. MARIA A NOTA LA) CADA DOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL .

122

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED

KERNEL

POR. E=LE TRABALHA PARA QUE MARIA A NOTE LA) CADA DOIS DIAS BEM . ENG. HE WORKS IN ORDER THAT MARY NOTICE IT THERE EVERY TWO DAYS WELL . NEGATIVE

RESULT

KERNEL POR. MARIA A NOTA LA) CADA DOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED

KERNEL

POR. E=LE TRABALHA SEM QUE MARIA A NOTE LA) CADA DOIS DIAS BEM . ENG. HE WORKS WITHOUT MARY )S NOTICING IT THERE EVERY TWO D AYS WELL . PREDICTIVE IF KERNEL POR. MARIA A NOTA LA) CADA DQIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

123

POR. E=LE TRABALHARA) SE MARIA A NOTAR LA) CADA DOIS DIAS B EM . ENG. HE WILL WORK IF MARY NOTICES IT THERE EVERY TWO DAYS W ELL . CONTRARY IF KERNEL POR. MARIA A NOTA LA) CADA OOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . ADDITIONAL KERNEL POR. EU 0 FAREI. ENG. I WILL DO IT. TRANSFORMED KERNEL POR. EU 0 FARIA SE MARIA A NOTASSE LA) CADA DOIS DIAS BEM . ENG. I WOULD DO IT IF MARY NOTICED IT THERE EVERY TWO DAYS WELL . FALSE MANNER KERNEL POR. MARIA A NOTA LA) CADA DOIS DIAS BEM . ENG. MARY NOTICES IT THERE EVERY TWO DAYS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E=LE TRABALHA COMO SE MARIA A NOTASSE LA) CADA DOIS 01 AS BEM . ENG. HE WORKS AS IF MARY NOTICED IT THERE EVERY TWO OAYS WE LL .

124

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ITERATION

5 KERNEL

POR. QUAISQUER OUTROS LIVROS TE ViAO TER ESTADO DANANDO . ENG. ANY OTHER BOOKS ARE GOING TO HAVE BEEN DAMAGING YOU . ONE STRING TRANSFORMATIONS PASSIVE POR. TU TERA.)S SIOO DANADA POR QUAISQUER OUTROS LIVROS . ENG. YOU WILL HAVE BEEN DAMAGED BY ANY OTHER BOOKS . COMPOUND POR. TU E ELAS TEREIS SIDO DANADAS POR QUAISQUER OUTROS LIV ROS . ENG. YOU AND THEY WILL HAVE BEEN DAMAGED BY ANY OTHER BOOKS ALTERNATE POR. TU OU ELAS T.EREIS SIDO DANADAS POR QUAISQUER OUTROS LI VROS . ENG. YOU pR THEY WILL HAVE BEEN DAMAGED BY ANY OTHER BOOKS NEGATIVE POR. TU OU ELAS N(AO TEREIS SIDO DANADAS POR NENHUNS OUTROS LIVROS . ENG. YOU OR THEY WILL NOT HAVE BEEN DAMAGED BY ANY OTHER BO OKS . INTERROGATIVE POR. POR QUE COISA TU OU ELAS N(AO TEREIS SIDO DANADAS .Q ENG. BY WHAT WILL YOU OR THEY NOT HAVE BEEN DAMAGED .Q TWO STRING TRANSFORMATIONS SENTENCE AS NOUN PHRASE

125

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR KERNEL POR.

QUAISQUER

OUTROS

ENG.

ANY

BOOKS A R E GOING TO HAVE

OTHER

KERNEL

LIVROS

CONTAINING

POR.

A VERDADE E )

ISTO.

ENG.

THE T R U T H

THIS.

IS

TE

TRANSFORMED POR.

A VERDADE E ) QUE STADO DANANDO .

ENG.

THE T R U T H I S THAT EN DAMAGING Y O U .

V(AO

NOUN

TER

ESTADO DANANDO

BEEN DAMAGING

.

YOU

.

PHRASE

KERNEL QUAISQUER

ANY

OUTROS

OTHER

SENTENCE- AS T I M E

LIVROS

BOOKS A R E

TE. V t A O

TER

GOING TO H A V E

E

BE

ELEMENT

KERNEL POR.

QUAISQUER

OUTROS

ENG.

ANY

BOOKS A R E

OTHER

KERNEL POR.

MARIA

ENG.

MARY

AQUI

B E HERE

TRANSFORMED POR.

MARIA ESTARA) I V E R E M ESTADO

ENG.

MARY W I L L ING YOU .

TE

GOING

CONTAINING

ESTARA) WILL

LIVROS

V(AO

TER

TO HAVE

TIME

BEEN

SENTENCE

DANANDO

DAMAGING

.

YOU

.

ELEMENT

AMANHlA. TOMORROW. KERNEL

AQUI QUANDO QUAISQUER DANANDO .

BE HERE

ESTADO

WHEN ANY

AS P L A C E

OTHER

OUTROS

LIVROS

BOOKS HAVE BEEN

TE

T

DAMAG

ELEMENT

KERNEL POR.

QUAISQUER

OUTROS

ENG.

ANY

BOOKS A R E

OTHER

KERNEL

LIVROS

TE VtAO

GOING

CONTAINING

TER

TO H A V E

PLACE

ESTADO

DANANDO

BEEN DAMAGING

ELEMENT

YOU

. .

126

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR.

J O ( A O COME AQUI .

ENG.

JOHN E A T S

HERE.

TRANSFORMED

KERNEL

POR.

J O ( A O COME ONDE QUAISQUER TADO OANANDO .

ENG.

JOHN E A T S WHERE ANY OTHER BOOKS ARE GOING TO HAVE DAMAGING YOU . SENTENCE

AS MANNER

OUTROS L I V R O S

TE V ( A O

TER

ES

BEEN

ELEMENT

KERNEL POR.

QUAISQUER

OUTROS L I V R O S

ENG.

ANY OTHER BOOKS A R E GOING TO HAVE DERIVED

TE V ( A O TER ESTADO DANANDO BEEN DAMAGING YOU

.

KERNEL

P O R . OS PANOS TE T E E M ESTADO DANANDO ENG.

.

.

THE CLOTHS HAVE B E E N OAMAGING YOU TRANSFORMED

.

KERNEL

POR.

QUAISQUER OUTROS L I V R O S TE V ( A O TER ESTADO DANANDO COM 0 OS PANOS TE TEEM ESTADO DANANDO .

ENG.

ANY OTHER BOOKS A R E GOING TO HAVE BEEN DAMAGING YOU K E THE C L O T H S H A V E B E E N DAMAGING YOU . O P T I O N A L ONE S T R I N G

LI

TRANSFORMATION

POR.

QUAISQUER OUTROS L I V R O S T E V ( A O T E R ES.TADO DANANDO COM 0 OS PANOS .

ENG.

ANY OTHER BOOKS A R E GOING TU H A V E B E E N DAMAGING YOU L I KE THE C L O T H S . I N T E R R O G A T I V E AS NOUN P H A S E INTERROGATIVE

POR.

POR QUE C O I S A

T U OU E L A S NCAO T E R E I S

ENG.

BY WHAT W I L L YOU OR THEY NOT H A V E BEEN DAMAGED K E R N E L C O N T A I N I N G NOUN P H R A S E

S I D O DANADAS .Q

.Q

127

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR.

EU S E I

ENG.

I

A

VERDADE.

KNOW THE

TRUTH.

TRANSFORMED POR.

EU S E I AS

EHG.

I

KERNEL

POR QUE C O I S A T U OU E L A S

KNOW BY WHAT YOU OR THEY W I L L

N(AO T E R E I S

S I D O OANAD

NOT H A V E B E E N

DAMAGED

CONCESSION KERNEL POR.

QUAISQUER OUTROS

ENG.

ANY OTHER BOOKS A R E GOING TO HAVE B t E N ADDITIONAL

POR.

E=LE

ENG.

HE W I L L

L I V R O S T E V-IAQ TER ESTADO DANANDO

.

DAMAGING YOU

.

KERNEL

TRABALHARA). WORK.

TRANSFORMED

KERNEL

POR.

E=LE T R A B A L H A R A ) MESMO QUE QUAISQUER TENHAM ESTADO DANANDO .

OUTROS L I V R O S

ENG.

HE W I L L WORK E V E N THOUGH ANY OTHER BOOKS HAVE SEEK A G I N G YOU .

TE DAM

CONDITION KERNEL POR.

QUAISQUER

OUTROS

L I V R O S T E V ( A O T E R ESTADO DANANDO

E N G . ANY OTHER BOOKS A R E GOING TO HAVE B E E N DAMAGING YOU ADDITIONAL POR.

E=LE

ENG.

HE W I L L

.

KERNEL

TRABALHARA). WORK.

TRANSFORMED POR.

.

KERNEL

E=LE T R A B A L H A R A ) CASO QUAISQUER OUTROS L I V R O S M ESTADO DANANDO .

TE

TBNHA

128

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. HE WILL WORK IN GASE ANY OTHER BOOKS HAVE BEEN DAMAGIN" G YOU . PURPOSE KERNEL POR. QUAISQUER OUTROS LIVROS T E VIAO TER ESTADO 0ANANDO

.

ENG. ANY OTHER BOOKS ARE GOING TO HAVE BEEN DAMAGING YOU . ADDITIONAL

KERNEL

POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) ANEM .

PARA QUE QUAISQUER OUTROS LIVROS TE D

ENG. HE WILL WORK IN ORDER THAT ANY OTHER BOOKS DAMAGE YOU NEGATIVE

RESULT

KERNEL POR. QUAISQUER OUTROS LIVROS TE V(AO TER ESTADO DANANDO

.

E N C . ANY OTHER BOOKS ARE GOING TO HAVE BEEN DAMAGING YOU ADDITIONAL

.

KERNEL

POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E * L E TRABALHARA) H

SEM QUE NENHUNS OUTROS LIVROS T E DANE

ENG. HE WILL WORK WITHOUT ANY OTHER BOOKS ) DAMAGING YOU . PREDICTIVE

IF

KERNEL POR.

QUAISQUER OUTROS LIVROS TE V(AO TER ESTADO DANANDO

.

129

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR ENG.

ANY

OTHER

BOOKS

A R E GOING TO HAVE

ADDITIONAL PÖR.

E=LE

ENG.

HE W I L L

BEEN

DAMAGING YOU

.

KERNEL

TRABALHARA). WORK.

TRANSFORMED

KERNEL

POR.

E = L E T R A B A L H A R A ) SE QUAISQUER ESTADO DANANDO .

ENG.

HE W I L L WORK I F

CONTRARY

ANY

OTHER

OUTROS L I V R O S

BOOKS HAVE B E E N

TE

TIVEREM

DAMAGING

YOU

IF

KERNEL POR.

QUAISQUER

ENG.

ANY OTHER BOOKS A R E GOING TO HAVE

OUTROS

ADDITIONAL POR.

EU 0

ENG.

I

LIVROS T E V(AO TER

ESTADO

DANANDO

.

BEEN DAMAGING YOU

.

KERNEL

FAREI.

WILL

DO

IT.

TRANSFORMED POR.

EU 0 F A R I A DO DANANDO

ENG.

I

KERNEL

SE QUAISQUER .

WOULD DO I T

FALSE

IF

ANY

OUTROS

OTHER

LIVROS

BOOKS

TE T I V E S S E M

ESTA

HAD B E E N DAMAGING

YOU

MANNER

KERNEL POR.

QUAISQUER

OUTROS

ENG.

ANY

BOOKS A R E

OTHER

ADDITIONAL POR.

E=LE

TRABALHARA).

ENG.

HE W I L L

WORK.

LIVROS

TE V(AO TER

GOING TO HAVE

KERNEL

ESTADO

DANANDO

BEEN DAMAGING YOU

. .

130

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

TRANSFORMED

KERNEL

POR.

E = L E T R A B A L H A R A ) COMO SE QUAISQUER V E S S E M ESTADO DANANDO .

ENG.

HE W I L L OU .

ITERATION

WORK A S I F

OUTROS L I V R O S

KERNEL

E N G . WE W I L L

ESTAR QUENTES

BE A B L E TO BE HOT

ONE S T R I N G

.

.

TRANSFORMATIONS

COMPOUND POR.

NO)S

PODEREMOS

ENG.

WE W I L L

ESTAR L I M P A S

BE A B L E

E QUENTES

TO BE C L E A N AND HOT

.

.

ALTERNATE POR.

NO)S PODEREMOS

ENG.

WE W I L L

ESTAR L I M P A S OU QUENTES

BE A B L E TO BE C L E A N OR HOT

.

.

NEGATIVE POR.

NO)S N ( A O PODEREMOS ESTAR L I M P A S

E N G . WE W I L L

NEM QUENTES

NOT BE A B L E TO BE C L E A N NOR HOT

INTERROGATIVE P O R . COMO N O ) S N(AO PODEREMOS ENG.

ESTAR

HOW W I L L WE NOT BE A B L E TO BE TWO S T R I N G SENTENCE KERNEL

II

ANY OTHER BOOKS HAD B E E N DAMAGING Y

6

P O R . NO) S PODEREMOS

TE

.Q .Q

TRANSFORMATIONS

AS NOUN P H R A S E

.

.

131

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR POR.

NO)S

PODEREMOS

ENG.

WE W I L L

BE

ESTAR

ABLE

KERNEL

TO

QUENTES

BE

HOT

CONTAINING

POR.

ISSO

ME

ENG.

THAT

INTERESTS

.

.

NOUN

PHRASE

INTERESSA. ME.

TRANSFORMED POR.

INTERESSA-ME

QUE

ENG.

IT

ME

INTERESTS

SENTENCE

KERNEL NO)S

THAT AS

PODEREMOS WE W I L L

TIME

BE

ESTAR ABLE

QUENTES TO

BE

.

HOT

.

ELEMENT

KERNEL POR.

NO)S

PODEREMOS

ENG.

WE W I L L

BE

ABLE

KERNEL POR.

MARIA

ENG.

MARY

BE

TO

AQUI HERE

TRANSFORMED POR.

MARIA •

ENG.

MARY

ESTARA)

WILL

BE

QUENTES

BE

HOT

CONTAINING

ESTARA) WILL

ESTAR

AQUI

HERE

SENTENCE

AS

.

.

TIME

ELEMENT

AMANH(A. TOMORROW. KERNEL QUANDO

WHEN

WE

PLACE

NO)S

ARE

PUDERMOS

ABLE

TO

ELEMENT

KERNEL POR.

NO)S

PODEREMOS

ENG.

WE W I L L

BE

ABLE

KERNEL POR.

JO(AO

ENG.

JOHN

COME EATS

ESTAR TO

BE

QUENTES HOT

CONTAINING

AQUI. HERE.

.

.

PLACE

ELEMENT

BE

ESTAR

HOT

QUENTES

.

132

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR TRANSFORMED

POR.

JOtAO

ENG.

JOHN

COME EATS

ONDE

NO)S

WHERE

SENTENCE

KERNEL POOEREMOS

WE W I L L AS

BE

MANNER

ESTAR

ABLE

TO

BE

QUENTES HOT

.

.

ELEMENT

KERNEL POR.

NO) S

POOEREMOS

ENG.

WE W I L L

BE

ABLE

DERIVED POR.

AS

ENG.

THE

MO=C)AS GIRLS

ESTAR TO

PODEM ARE

N O ) S POOEREMOS AR Q U E N T E S .

ENG.

WE W I L L B E E HOT .

PODEREMOS

WE W I L L

BE

TO

.

TO

BE

HOT

.

.

QUENTES

BE

ONE

HOT

TO

HOT

AS

AS

THE

MO=C)AS

GIRLS

COMO

LIKE

NOUN

AS

THE

MO=C)AS

GIRLS

PHASE

INTERROGATIVE POR.

COMO N O ) S

ENG.

HOW W I L L

N(AO WE NOT

KERNEL POR.

EU

ENG.

I

SEI KNOW

A

PODEREMOS BE

CONTAINING

EU

SEI

ESTAR TO

BE

NOUN

.Q .Q

PHRASE

VERDADE.

THE

TRUTH.

TRANSFORMED POR.

ABLE

COMO

NO)S

KERNEL N(AO

ARE

PODEM

ABLE

TRANSFORMATION

QUENTES

BE

INTERROGATIVE

COMO

LIKE

STRING

ESTAR

ABLE

QUENTES

KERNEL

ESTAR

ABLE

OPTIONAL

ENG.

HOT

ESTAR

ABLE

POR.

NO)S

BE

.

KERNEL

TRANSFORMED

POR.

QUENTES

PODEREMOS

ESTAR

.

.

.

EST

TO

B

133

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR ENG.

I

KNOW HOW WE W I L L

NOT

BE A B L E

TO

BE

.

CONCESSION KERNEL POR.

NO)S

PODEREMOS

ENG.

WE W I L L

BE

ESTAR

ABLE

TO BE HOT

ADDI1IONAL POR.

E-LE

ENG.

HE W I L L

E=LE

.

.

KERNEL

TRABALHARA). WORK.

TRANSFORMED POR.

QUENTES

TRABALHARA)

KERNEL MESMO QUE

NO)S

POSSAMOS

ESTAR

QUENTES

m

ENG.

HE W I L L

WORK E V E N

THOUGH WE ARE

ABLE

TO BE

HOT

.

CONDITION KERNEL POR.

NO)S

PODEREMOS

ENG.

WE W I L L

ESTAR

BE A B L E

TO BE HOT

ADDITIONAL POR.

E=LE

ENG.

HE W I L L

QUENTES

.

.

KERNEL

TRABALHARA). WORK.

TRANSFORMED POR.

E=LE T R A B A L H A R A )

ENG.

HE W I L L

WORK I N

KERNEL CASO N O ) S POSSAMOS

C A S E WE ARE

ABLE

PURPOSE KERNEL POR.

NO)S

PODEREMOS

ENG.

WE W I L L

BE

ESTAR

ABLE

ADDITIONAL

TO BE

QUENTES HOT

KERNEL

.

.

ESTAR

TO BE

HOT

QUENTES .

.

134

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED

KERNEL

POR. E=LE TRABALHARA) PARA QUE NO)S POSSAMtJ-S ESTAR -QUENTES • ENG. HE WILL WORK IN ORDER THAT WE CAN BE HOT . NEGATIVE

RESULT

KERNEL POR. NO)S PODEREMOS ESTAR QUENTES ENG. WE WILL BE ABLE TO BE HOT . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED

KERNEL

POR. E=LE TRABALHARA) SEM QUE NO)S POSSAMOS ESTAR QUENTES . ENG. HE WILL WORK WITHOUT OUR BEING ABLE TO BE HOT . PREDICTIVE IF KERNEL POR. NO)S PODEREMOS ESTAR QUENTES . ENG. WE WILL BE ABLE TO BE HOT . ADDITIONAL

KERNEL

POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED

KERNEL

POR. E=LE TRABALHARA) SE NO)S PUDERMOS ESTAR QUENTES . ENG. HE WILL WORK IF WE ARE ABLE TO BE HOT .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

135

CONTRARY IF KERNEL POR. NO)S PODEREMOS ESTAR QUENTES . ENG. WE WILL BE ABLE TO BE HOT . ADDITIONAL KERNEL POR. EU 0 FAREI. ENG. I WItL DO IT. TRANSFORMED KERNEL POR. EU O FARIA SE NO)S PUDEJSSEMOS ESTAR QUENTES . ENG. I WOULD DO IT IF WE WERE ABLE TO BE HOT • FALSE MANNER KERNEL POR. NO)S PODEREMOS ESTAR QUENTES . ENG. WE WILL BE ABLE TO BE HOT . ADDITIONAL KERNEL POR.

TRABALHARA).

ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) COMO SE NO)S PUDEJSSEMOS ESTAR QUENTE S . ENG. HE WILL WORK AS IF WE WERE ABLE TO BE HOT . COMPARATIVE KERNEL POR. NO)S PODEREMOS ESTAR QUENTES . ENG. WE WILL BE ABLE TO BE HOT . DERIVED KERNEL POR. DUAS M0= C)AS PODER(AO ESTAR QUENTES .

136

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. TWO GIRLS HILL BE ABLE TO BE HOT . TRANSFORMED KERNEL POR. NO)S PODEREMOS ESTAR MAIS QUENTES DO QUE DUAS MO=C)AS • ENG. WE WILL BE ABLE TO BE MORE HOT THAN TWO GIRLS SUPERLATIVE KERNEL POR. NO)S PODEREMOS ESTAR QUENTES . ENG. WE WILL BE ABLE TO BE HOT . DERIVED KERNEL POR. OUTRAS CRIATURAS PODERIAO ESTAR QUENTES . ENG. OTHER CREATURES WILL BE ABLE TO BE HOT . TRANSFORMED KERNEL POR. NO)S PODEREMOS SER AS MAIS QUENTES . ENG. WE WILL BE ABLE TO BE THE HOTTEST .

ITERATION

7

KERNEL POR. DOM ROBERTO ESTARA) QUENTE ATRA)S TO=DAS ESTAS OUTRAS CASAS HOJE . ENG. ROBERT WILL BE HOT BEHIND ALL THESE OTHER HOUSES TODAY

ONE STRING TRANSFORMATIONS COMPOUND POR. DOM ROBERTO ESTARA) LIMPO E QUENTE ATRA)S TO=DAS ESTAS OUTRAS CASAS HOJE . ENG. ROBERT WILL BE CLEAN AND HOT BEHIND ALL THESE OTHER HO USES TODAY .

137

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ALTERNATE POR.

DOM ROBERTO E S T A R A ) L I M P O OU QUENTE A T R A ) S S OUTRAS C A S A S HOJE .

ENG.

ROBERT W I L L BE C L E A N OR HOT B E H I N D A L L S E S TODAY .

TO=DAS

ESTA

T H E S E OTHER HOU

COMPOUND POR.

DOM ROBERTO E S T A R A ) L I M P O OU QUENTE A T R A ) S S OUTRAS C A S A S HOJE E AMANHIA .

ENG.

ROBERT W I L L BE C L E A N OR HOT B E H I N D A L L SES TODAY AND TOMORROW .

TO=DAS

ESTA

T H E S E OTHER

HOU

ALTERNATE POR.

DOM ROBERTO E S T A R A ) L I M P O OU QUENTE A T R A ) S S OUTRAS C A S A S HOJE OU AMANHfA .

ENG.

ROBERT W I L L BE C L E A N OR HOT B E H I N D A L L SES TODAY OR TOMORROW .

TO=DAS

ESTA

T H E S E OTHER

HOU

NEGATIVE POR.

DOM ROBERTO N I A O E S T A R A ) L I M P O NEM QUENTE A T R A ) S S E S T A S OUTRAS CASAS HOJE NEM AMANHIA .

ENG.

ROBERT W I L L NOT B E C L E A N NOR HOT B E H I N D A L L R HOUSES TODAY NOR TOMORROW .

TO=DA

THESE

OTHE

ESTAS

OUTR

INTERROGATIVE POR.

COMO DOM ROBERTO N ( A O E S T A R A ) AS C A S A S H O J E NEM AMANHIA . Q

ENG.

HOW WILL ROBERT NOT BE B E H I N D A L L ODAY NOR TOMORROW . Q TWO S T R I N G

A T R A J S TO=DAS

T H E S E OTHER

HOUSES T

TRANSFORMATIONS

MODIFICATION KERNEL POR.

DOM ROBERTO E S T A R A ) C A S A S HOJE .

ENG.

ROBERT WILL

QUENTE A T R A ) S

BE HOT B E H I N D A L L

TO=DAS

T H E S E OTHER

ESTAS

OUTRAS

HOUSES

TODAY

138

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR DERIVED

KERNEL

POR.

TODO 0 OUTRO HOMEM PARA DCM ROBERTO .

ENG.

ALL ERT

THE .

OTHER

MAN

TRANSFORMED

TEM E S T A D O

HAS

BEEN

PROMETENDO

PROMISING

EACH

TO=DA

MULHER

WOMAN TO

ROB

KERNEL

POR.

DOM ROBERTO P A R A QUEM TODO 0 OUTRO HOMEM TEM ESTADO PR OMETENDO T O - D A MULHER E S T A R A ) QUENTE A T R A ) S TO=DAS EST AS OUTRAS CASAS H O J E .

ENG.

ROBERT TO WHOM A L L THE OTHER MAN HAS B E E N P R O M I S I N G EA CH WOMAN W I L L BE HOT B E H I N D A L L T H E S E OTHER HOUSES TOD AY . SENTENCE

AS

NOUN

PHRASE

KERNEL POR.

DOM ROBERTO E S T A R A ) CASAS H O J E .

ENG.

ROBERT

WILL

BE

KERNEL POR.

EU

ENG.

I

SEI KNCW

A

HOT

QUENTE

BEHIND

CONTAINING

ATRA)S

ALL

NOUN

TO=DAS

THESE

OTHER

ESTAS

OUTRAS

HOUSES

TODAY

PHRASE

VERDADE.

THE

TRUTH.

TRANSFORMED POR.

EU S E I QUE TAS OUTRAS

ENG.

I KNCW HOUSES

KERNEL

DOM ROBERTO E S T A R A ) CASAS HOJE .

THAT ROBERT TODAY . SENTENCE

AS

WILL

TIME

BE

HOT

QUENTE

ATRA)S

BEHIND

ALL

TO=DAS

THESE

ES

OTHER

ELEMENT

KERNEL POR.

DOM ROBERTO E S T A R A ) CASAS H O J E .

ENG.

ROBERT

WILL

BE

HOT

QUENTE

BEHIND

ATRA)S

ALL

TO=DAS

THESE

OTHER

ESTAS

OUTRAS

HOUSES

TODAY

139

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR KERNEL POR.

MARIA

ENG.

MARY

CONTAINING

ESTARA) WILL

BE

AQUI

MARIA TRA)S

ENG.

MARY W I L L BE OTHER HOUSES

HERE

TOMORROW. KERNEL

ESTARA) ACUI TO=DAS E S T A S

QUANDO OUTRAS

H E R E WHEN TODAY .

SENTENCE

ELEMENT

AMANHIA.

TRANSFORMED POR.

TIME

AS

DOM R O B E R T O E S T I V E R CASAS HOJE .

ROBERT

PLACE

IS

HOT

BEHIND

QUENTE

ALL

A

THESE

ELEMENT

KERNEL POR.

DOM R O B E R T O E S T A R A ) C A S A S HOJE .

ENG.

ROBERT

WILL

BE

KERNEL POR.

JO(AO

ENG.

JOHN

COME EATS

HOT

QUENTE

BEHIND

CONTAINING

ATRA)S

ALL

PLACE

TO^DAS

THESE

HOUSES

TODAY

ELEMENT

HERE. KERNEL

POR.

J O ( A O COME O N D E AS E S T A S OUTRAS

DOM R O B E R T O E S T A R A ) CASAS HOJE .

ENG.

JOHN

ROBERT

HER

OTHER

OUTRAS

AQUI.

TRANSFORMED

EATS

ESTAS

WHERE

HOUSES

TODAY

SENTENCE

WILL

BE

HOT

QUENTE

BEHIND

ATRA)S

ALL

TO=D

THESE

O.T

.

AS

MANNER

ELEMENT

KERNEL POR.

DOM R O B E R T O F S T A R A ) CASAS HOJE .

ENG.

ROBERT

WILL

BE

DERIVED

HOT

QUENTE

BEHIND

KERNEL

ATRA)S

ALL

TO=DAS

THESE

OTHER

ESTAS

OUTRAS

HOUSES

TODAY

140

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

PGR. 0 M0=C)0 ESTA) QUENTE . ENG. THE LAD IS HOT . TRANSFORMED

KERNEL

POR. DOM ROBERTO ESTARA) QUENTE ATRAJS TO=DAS ESTAS OUTRAS CASAS HOJE COMO 0 MO^CJO ESTA) QUENTE . ENG. ROBERT WILL BE HOT BEHIND ALL THESE OTHER HOUSES TODAY LIKE THE LAD IS HOT . OPTIONAL ONE STRING

TRANSFORMATION

POR. DOM ROBERTO ESTARA) QUENTE ATRA)S TO=DAS ESTAS OUTRAS CASAS HOJE COMO 0 M0=C)0 . ENG. ROBERT WILL BE HOT BEHIND ALL THESE OTHER HOUSES TODAY LIKE THE LAD . INTERROGATIVE AS NOUN

PHASE

INTERROGATIVE POR. COMO DOM ROBERTO N(AO ESTARA) ATRA)S TO=DAS ESTAS OUTR AS CASAS HOJE NEM A M A N H U .Q ENG. HOW WILL ROBERT NOT BE BEHIND ALL THESE OTHER HOUSES T ODAY NOR TOMORROW .Q KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNCW THE TRUTH. TRANSFORMED

KERNEL

POR. EU SEI COMO DOM ROBERTO N(AO ESTARA) ATRAJS TO=DAS EST AS OUTRAS CASAS HOJE NEM AMANHIA . ENG. I KNCW HOW ROBERT WILL NOT BE BEHIND ALL THESE OTHER H OUSES TODAY NOR TOMORROW . CONCESSION KERNEL POR. DOM ROBERTO ESTARA) QUENTE ATRAJS TQ=DAS ESTAS OUTRAS CASAS HOJE . ENG. ROBERT WILL BE HOT BEHIND ALL THESE OTHER HOUSES TODAY

COMPUTER OUTPUT PROM PROGRAMMED GRAMMAR

ADDITIONAL i'OR. £ = L E

KERNEL

TRABALHARA).

feMG. HE W I L L

WORK.

TRANSFORMED POR.

141

KERNEL

E = L E TRABALHARA) MESMO QUE DOM ROBERTO E S T E J A QUENTE A T R A ) S TO= DAS E S T A S OUTRAS C A S A S HOJE .

ENG. HE WILL WORK EVEN THOUGH ROBERT I S HOT B E H I N D ALL E OTHER HOUSES TODAY .

THES

CONDITION KERNEL POR. DOM ROBERTO E S T A R A ) C A S A S HOJE .

QUENTE A T R A J S TO=DAS E S T A S

ENG. ROBERT WILL BE HOT B E H I N D A L L ADDITIONAL POR. E = L E

OUTRAS

THESE OTHER HOUSES

TODAY

KERNEL

TRABALHARA).

ENG. HE W I L L

WORK.

TRANSFORMED

KERNEL

POR. E = L E TRABALHARA) CASO DOM ROBERTO E S T E J A QUENTE TO=DAS E S T A S OUTRAS CASAS HOJE . ENG. HE W I L L WORK I N C A S E ROBERT HER HOUSES TODAY .

ATRA)S

I S HOT B E H I N D ALL T H E S E OT

PURPOSE KERNEL POR. DOM ROBERTO E S T A R A ) C A S A S HOJE .

QUENTE A T R A ) S

TO=DAS E S T A S

OUTRAS

ENG. ROBERT W I L L BE HOT BEHIND A L L T H E S E OTHER HOUSES TODAY ADDITIONAL

KERNEL

142

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) PARA QUE DOM ROBERTO ESTEJA QUENTE AT RAJS TO= DAS ESTAS OUTRAS CASAS HOJE . ENG. HE WILL WORK IN ORDER THAT ROBERT BE HOT BEHIND ALL TH ESE OTHER HOUSES TODAY . NEGATIVE RESULT KERNEL POR. DOM ROBERTO ESTARA) QUENTE ATRA)S TO=DAS ESTAS OUTRAS CASAS HOJE . ENG. ROBERT WILL BE HOT BEHIND ALL THESE OTHER HOUSES TODAY ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SEM QUE DOM ROBERTO ESTEJA QUENTE ATR A)S TO=DAS ESTAS OUTRAS CASAS HOJE . ENG. HE WILL WORK WITHOUT ROBERT )S BEING HOT BEHIND ALL TH ESE OTHER HOUSES TODAY . PREDICTIVE IF KERNEL POR. DOM ROBERTO ESTARA) QUENTE ATRA)S TO=DAS ESTAS OUTRAS CASAS HOJE . ENG. ROBERT WILL BE HOT BEHIND ALL THESE OTHER HOUSES TODAY ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK.

143

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

TRANSFORMED

KERNEL

POR.

E=LE T R A B A L H A R A ) S E DOM ROBERTO E S T I V E R TO=DAS E S T A S OUTRAS C A S A S HOJE .

ENG.

HE WILL WORK I F OUSES TODAY . CONTRARY

ROBERT

IS

HOT B E H I N D A L L

QUENTE

ATRA)S

THESE OTHER H

IF

KERNEL POR.

DOM ROBERTO E S T A R A ) C A S A S HOJE .

ENG.

ROBERT WILL

EU 0

ENG.

I

ATRA)S

TO=DAS E S T A S

OUTRAS

BE HOT BEHIND A L L T H E S E OTHER HOUSES

ADDITIONAL POR.

QUENTE

TODAY

KERNEL

FAREI.

WILL

DO

IT.

TRANSFORMED

KERNEL

POR.

EU 0 F A R I A SE DOM ROBERTO E S T I V E S S E AS E S T A S OUTRAS C A S A S HOJE .

ENG.

I WOULD DO I T I F ROBERT WERE HOT B E H I N D A L L R HOUSES TODAY . FALSE

QUENTE

ATRA)S

TO=D

THESE

OTHE

MANNER

KERNEL POR.

DOM ROBERTO E S T A R A ) C A S A S HOJE .

ENG.

ROBERT WILL

E=LE

ENG.

HE W I L L

TO=DAS E S T A S

OUTRAS

T H E S E OTHER HOUSES

TODAY

KERNEL

TRABALHARA). WORK.

TRANSFORMED POR.

ATRA)S

BE HOT BEHIND A L L

ADDITIONAL POR.

QUENTE

KERNEL

E=LE T R A B A L H A R A ) COMO SE DOM ROBERTO E S T I V E S S E A T R A ) S TO=DAS E S T A S OUTRAS CASAS HOJE .

QUENTE

144 ENG.

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR HE W I L L WORK AS I F HER HOUSES TOOAY .

ROBERT

WERE

HOT

BEHIND

ALL

THESE

OT

COMPARATIVE KERNEL POR.

DOM ROBERTO E S T A R A ) CASAS H O J E .

ENG.

ROBERT

WILL

BE HOT

DERIVED

QUENTE

BEHIND

ATRAJS

ALL

TO=DAS

THESE

ESTAS

OTHER

OUTRAS

HOUSES

TODAY

KERNEL

POR.

ALGUM ALUNO E S T A R A ) CASAS H O J E .

ENG.

SOME STUDENT TODAY .

QUENTE A T R A J S

WILL

BE HOT

TRANSFORMED

KERNEL

TO=DAS

BEHIND A L L

THESE

POR.

DOM ROBERTO E S T A R A ) MAIS QUENTE A T R A ) S TRAS CASAS H O J E DO QUE NENHUM ALUNO .

ENG.

ROBERT W I L L BE TODAY THAN ANY

MORE HOT B E H I N D STUDENT .

ALL

ESTAS

OUTRAS

OTHER

TO=DAS

HOUSES

ESTAS

OU

T H E S E OTHER

HOUSES

TO=DAS E S T A S

OUTRAS

SUPERLATIVE KERNEL POR.

DOM ROBERTO E S T A R A ) CASAS H O J E .

ENG.

ROBERT

WILL

BE

DERIVED

HOT

QUENTE

BEHIND

(JM OUTRO SER E S T A R A ) CASAS H O J E .

ENG.

ANCTHER S TODAY

WILL

TRANSFORMED POR.

ALL

THESE

OTHER HOUSES

TODAY

KERNEL

POR.

BEING .

ATRA)S

QUENTE

BE HOT

ATRAJS

BEHIND

TO=DAS

ALL

THESE

ESTAS

OTHER

OUTRAS

HOUSE

KERNEL

DOM ROBERTO S E R A ) 0 MAIS TRAS C A S A S HOJE .

QUENTE

ATRA)S

TO=DAS

ESTAS

OU

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG.

ROBERT W I L L ES TODAY .

ITERATION

BE THE HOTTEST

BEHIND ALL

T H E S E OTHER

8 KERNEL

POR.

POUCA

ENG.

LITTLE

A)GUA

ESTEVE

LIMPA

WATER WAS C L E A N ONE S T R I N G

.

.

TRANSFORMATIONS

COMPOUND POR.

POUCA

ENG.

LITTLE

A)GUA

ESTEVE

QUENTE

E LIMPA

WATER WAS HOT AND C L E A N

.

.

ALTERNATE POR.

POUCA

ENG.

LITTLE

A)GUA

E S T E V E QUENTE OU LIMPA

WATER WAS HOT OR CLEA.N

.

.

NEGATIVE POR.

POUCA A J ê U A N I A O

ENG.

LITTLE

E S T E V E QUENTE NEM L I M P A

WATER WAS NOT HOT NOR C L E A N INTERROGATIVE

POR.

COMO POUCA

A)GUA N(AO

ENG.

HOW WAS L I T T L E

ESTEVE

WATER NOT

TWO S T R I N G

.Q

TRANSFORMATIONS

MODIFICATION KERNEL POR.

POUCA A ) G U A

ENG.

LITTLE

.Q

ESTEVE LIMPA

WATER WAS C L E A N

.

.

.

.

145

HOUS

146

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

DERIVED KERNEL POR. PCUCA A)GUA PODE ARRANHAR AS FILIPINAS . ENG. LITTLE WATER IS ABLE TO SCRATCH THE PHILIPPINES . TRANSFORMED KERNEL POR. POUCA A)GUA QUE PODE ARRANHAR AS FILIPINAS ESTEVE LIMP A . ENG. LITTLE WATER WHICH IS ABLE TO SCRATCH THE PHILIPPINES WAS CLEAN DETERIORATING . PREDICTIVE IF KERNEL POR. OS ESTADOS UNIDOS DETERIORAM . ENG. THE UNITED STATES DETERIORATE .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

241

ADDITIONAL KERNEL POR. E=LÉ TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SE OS ESTADOS UNIDOS DETERIORAREIS . ENG. HE WILL WORK IF THE UNITED STATES DETERIORATE . CONTRARY IF KERNEL POR. OS ESTADOS UNIDOS DETERIORAM . ENG. THE UNITED STATES DETERIORATE . ADDITIONAL KERNEL POR. EU O FAREI. ENG. I WILL DO IT. TRANSFORMED KERNEL POR. EU O FARIA SE OS ESTADOS UNIDOS DETERIORASSEM . ENG. I WOULD DO IT IF THE UNITED STATES DETERIORATED . FALSE MANNER KERNEL POR. OS ESTADOS UNIDOS DETERIORAM . ENG. THE UNITED STATES DETERIORATE . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E=LE TRABALHA COMO SE OS ESTADOS UNIDOS DETERIORASSEM ENG. HE WORKS AS IF THE UNITED STATES DETERIORATED

242 ITERATION

COMPUTER OUTPUT FROM P R O G R A M M E D G R A M M A R 23 KERNEL

POR. A A)SI A FOI A OUTRA COISA

.

ENG. ASIA WAS THE OTHER THING . ONE STRING

TRANSFORMATIONS

COMPOUND POR. A A)SIA FOI A OUTRA COISA E QUASE UMA COISA

.

ENG. ASIA W A S THE OTHER THING ANO ALMOST ONE THING

.

ALTERNATE POR. A A)SI A FOI A OUTRA COISA OU QUASE UMA COISA . ENG. ASIA WAS THE OTHER THING OR ALMOST ONE THING . NEGATIVE POR. A A)SI A N(AO FOI A OUTRA COISA NEM QUASE UMA COISA ENG. ASIA WAS NOT THE OTHER THING NOR ALMOST ONE THING INTERROGATIVE POR. 0 QUE A A)SIA NIAO FOI ENG. WHAT WAS ASIA NOT TWO STRING

.Q

.Q TRANSFORMATIONS

MODIFICATION KERNEL POR. A A)SIA FOI A OUTRA COISA ENG. ASIA WAS THE OTHER THING DERIVED

. .

KERNEL

POR. E=LES QUEI MAM A OUTRA COISA

.

. .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. THEY BURN THE OTHER THING . TRANSFORMED KERNEL POR. A A)SIA FOI A OUTRA COISA QUE E=LES QUEIMAM . ENG. ASIA WAS THE OTHER THING THAT THEY BURN . SENTENCE AS NOUN PHRASE KERNEL POR. A A)SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI QUE A AJSIA FOI A OUTRA COISA . ENG. I KNOW THAT ASIA WAS THE OTHER THING . SENTENCE AS TIME ELEMENT KERNEL POR. A A)SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . KERNEL CONTAINING TIME ELEMENT POR. EU ESTIVE LA) ONTEM. ENG. I WAS THERE YESTERDAY. TRANSFORMED KERNEL POR. EU ESTIVE LA) QUANDO A A)SIA FOI A OUTRA COISA . ENG. I WAS THERE WHEN ASIA WAS THE OTHER THING . SENTENCE AS PLACE ELEMENT KERNEL

243

244

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

PCJR. A A) SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . KERNEL CONTAINING PLACE ELEMENT POR. JOCAO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JO(AO COME ONDE A A)SIA FOI A OUTRA COISA . ENG. JOHN EATS WHERE ASIA WAS THE OTHER THING . SENTENCE AS MANNER ELEMENT KERNEL POR. A A)SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . DERIVED KERNEL POR. A COISA PODE SER A OUTRA COISA . ENG. THE THING IS ABLE TO BE THE OTHER THING . TRANSFORMED KERNEL POR. A A)SI A FOI A OUTRA COISA COMO A COISA PODE SER A OUTR A COISA . ENG. ASIA WAS THE OTHER THING LIKE THE THING IS ABLE TO BE THE OTHER THING . OPTIONAL ONE STRING TRANSFORMATION POR. A A)SI A FOI A OUTRA COISA COMO A COISA . ENG. ASIA WAS THE OTHER THING LIKE THE THING . INTERROGATIVE AS NOUN PHASE INTERROGATIVE POR. 0 QUE A A)SIA NIAO FOI .0 ENG. WHAT WAS ASIA NOT .Q

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

245

KERNEL CONTAINING NOUN PHRASE PCJR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI 0 QUE A A)SIA NIAO FOI . ENG. I KNOW WHAT ASIA WAS NOT . CONCESSION KERNEL POR. A A)SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU MESMO QUE A AJSIA FO=SSE A OUTRA COISA ENG. HE WORKED EVEN THOUGH ASIA WAS THE OTHER THING . CONDITION KERNEL POR. A AJSIA FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU CASO A A)SIA FO=SSE A OUTRA COISA .

246

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. HE WORKED IN CASE ASIA WAS THE OTHER THING . PURPOSE KERNEL POR. A A)SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU PARA QUE A A)SIA FO=SSE A OUTRA COISA . ENG. HE WORKED IN ORDER THAT ASIA BE THE OTHER THING . NEGATIVE RESULT KERNEL POR. A A)SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU SEM QUE A A)SIA FO=SSE A OUTRA COISA . ENG. HE WORKED WITHOUT ASIA )S BEING THE OTHER THING . CONTRARY IF KERNEL POR. A A)SIA FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . ADDITIONAL KERNEL

COMPUTER OUTPUT FROM PROGRAMMED G R A M M A R

247

POR. EU IA FAZE=—LO. ENG. I WAS GOING TO DO IT. TRANSFORMED

KERNEL

POR. EU 0 TERIA FEITO SE A A)SIA TIVESSE SIDO A OUTRA COISA ENG. I WOULD HAVE DONE IT IF ASIA HAD BEEN THE OTHER THING FALSE

MANNER

KERNEL POR. A A)SI A FOI A OUTRA COISA . ENG. ASIA WAS THE OTHER THING . ADDITIONAL

KERNEL

POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED

KERNEL

POR. E=LE TRABALHOU COMO SE A A)SIA FO=SSE A OUTRA COISA . ENG. HE WORKED AS IF ASIA WERE THE OTHER THING .

ITERATION

24 KERNEL

POR. QUASE TODOS OS HOMENS ANDAR(AO AGORA COM POUCA DADE .

DIFICUL

ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y . ONE STRING

TRANSFORMATIONS

COMPOUND POR. QUASE TODOS OS HOMENS E VINTE MULHERES ANDARIAO AGORA COM POUCA DIFICULDADE .

248

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. ALMOST ALL THE MEN AND TWENTY WOMEN WILL WALK NOW WITH LITTLE DIFFICULTY . ALTERNATE POR. QUASE TODOS OS HOMENS OU VINTE MULHERES ANDAR(AO AGORA COM POUCA DIFICULDADE . ENG. ALMOST ALL THE MEN OR TWENTY WOMEN WILL WALK NOW WITH LITTLE DIFFICULTY . COMPOUND POR. QUASE TODOS OS HOMENS OU VINTE MULHERES ANDARi AO AGORA E AMANHIA COM POUCA DIFICULDADE . ENG. ALMOST ALL THE MEN OR TWENTY WOMEN WILL WALK NOW AND T OMORROW WITH LITTLE DIFFICULTY . ALTERNATE POR. QUASE TODOS OS HOMENS OU VINTE MULHERES ANDAR(AO AGORA E AMANH(A COM POUCA DIFICULDADE OU BEM . ENG. ALMOST ALL THE MEN OR TWENTY WOMEN WILL WALK NOW AND T OMORROW WITH LITTLE DIFFICULTY OR WELL . NEGATIVE POR. QUASE TODOS OS HOMENS OU VINTE MULHERES NiAO ANDAR(AO AGORA NEM AMANH(A COM POUCA DIFICULDADE NEM BEM . ENG. ALMOST ALL THE MEN OR TWENTY WOMEN WILL NOT WALK NOW N OR TOMORROW WITH LITTLE DIFFICULTY NOR WELL . INTERROGATIVE POR. COMO QUASE TODOS OS HOMENS OU VINTE MULHERES N(AO ANDA R(AO AGORA NEM AMANHCA .Q ENG. HOW WILL ALMOST ALL THE MEN OR TWENTY WOMEN NOT WALK N OW NOR TOMORROW .Q TWO STRING TRANSFORMATIONS MODIFICATION KERNEL POR. QUASE TODOS OS HOMENS ANDAR(AO AGORA COM POUCA DIFICUL DADE .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

249

ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y . DERIVED KERNEL POR. NO)S PROMETEMOS QUASE TODOS OS HOMENS PARA QUASE TODO 0 C(AO . ENG. WE PROMISE ALMOST ALL THE MEN TO ALMOST ALL THE DOG . TRANSFORMED KERNEL POR. QUASE TODOS OS HOMENS QUE NO)S PROMETEMOS PARA QUASE T ODO 0 C(AO ANDARC AO AGORA COM POUCA DIFICULDADE . ENG. ALMOST ALL THE MEN WHOM WE PROMISE TO ALMOST ALL THE D OG WILL WALK NOW WITH LITTLE DIFFICULTY . SENTENCE AS NOUN PHRASE KERNEL POR. QUASE TODOS OS HOMENS A N D A R U O AGORA COM POUCA DIFICUL DADE . ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y . KERNEL CONTAINING NOUN PHRASE POR. ISSO ME INTERESSA. ENG. THAT INTERESTS ME. TRANSFORMED KERNEL POR. INTERESSA-ME QUE QUASE TODOS OS HOMENS ANDARC AO AGORA COM POUCA DIFICULDADE . ENG. IT INTERESTS ME THAT ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULTY . SENTENCE AS TIME ELEMENT KERNEL POR. QUASE TODOS OS HOMENS ANDARIAO AGORA COM POUCA DIFICUL DADE . ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y .

250

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR KERNEL

POR.

MARIA

ENG.

MARY

CONTAINING

ESTARA) WILL

BE

AQUI HERE

TRANSFORMED

TIME

ELEMENT

AMANHIA. TOMORROW. KERNEL

POR.

MARIA E S T A R A ) AQUI QUANDO QUASE TODOS M AGORA COM POUCA D I F I C U L D A D E .

OS HOMENS

ENG.

MARY W I L L BE HERE WHEN ALMOST H LITTLE DIFFICULTY .

MEN WALK NOW WIT

SENTENCE

AS

PLACE

ALL

THE

ANDARE

ELEMENT

KERNEL POR.

QUASE TODOS OS HOMENS DADE .

ENG.

ALMOST Y

ALL

THE

ANDAR(AO

MEN W I L L

AGORA COM POUCA

WALK NOW WITH

LITTLE

DIFICUL

DIFFICULT

. KERNEL COME

CONTAINING

PLACE

POR.

JO(AO

ENG.

JOHN E A T S

POR.

J O ( A O COME ONDE QUASE TODOS OM POUCA D I F I C U L D A D E .

ENG.

J O H N E A T S WHERE ALMOST LITTLE DIFFICULTY .

ELEMENT

AQUI. HERE.

TRANSFORMED

SENTENCE

KERNEL

ALL

AS MANNER

OS HOMENS

THE

ANDAR(AO

MEN W I L L

AGORA C

WALK NOW W I T H

ELEMENT

KERNEL POR.

QUASE TODOS OS HOMENS A N D A R t A O DADE .

ENG.

ALMOST Y

ALL

THE

. DERIVED

POR.

MEN W I L L

OS M O O O S

KERNEL

ANDAM

.

AGORA COM POUCA

WALK NOW WITH

LITTLE

DIFICUL

DIFFICULT

251

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR ENG« T H E

LADS

WALK

.

TRANSFORMED

KERNEL

POR.

Q U A S E TODOS OS HOMENS A N D A R t A O DADE COMO OS M O C ) O S ANDAM .

ENG.

ALMOST Y LIKE

ALL THE

T H E MEN W I L L L A D S WALK .

OPTIONAL

ONE

WALK

STRING

POR.

QUASE T O D O S OS HOMENS DADE COMO OS M O O O S .

ENG.

ALMOST

ALL

THE

Y LIKE

THE

LADS

NOW WITH

LITTLE

DIFICUL

DIFFICULT

TRANSFORMATION

ANDAR1AO

MEN W I L L

AGORA COM POUCA

WALK

AGORA

COM POUCA

NOW WITH L I T T L E

DIFICUL

DIFFICULT

.

INTERROGATIVE

AS

NOUN

PHASE

INTERROGATIVE POR.

COMO QUASE R ( A O AGORA

T O D O S OS HOMENS NEM AMANH(A . 0

ENG.

HOW W I L L A L M O S T OW NOR TOMORROW KERNEL

POR.

EU S E I

A

ENG.

I

THE

KNOW

ALL .Q

THE

CONTAINING

OU V I N T E

MEN OR

NOUN

MULHERES

TWENTY

NtAO

WOMEN NOT

ANDA

WALK

N

PHRASE

VERDADE. TRUTH.

TRANSFORMED POR.

EU S E I COMO AO A N D A R t A O

ENG.

I

QUASE AGORA

KERNEL TODOS OS HOMENS NEM AMANHtA .

KNOW HOW A L M O S T A L L T H E WALK NOW NOR TOMORROW .

OU V I N T E

MEN OR TWENTY

MULHERES

WOMEN W I L L

Ni

NOT

CONCESSION KERNEL POR.

Q U A S E TODOS DADE .

ENG.

ALMOST Y .

ALL

OS

THE

HOMENS

ANDARtAO

MEN W I L L

WALK

AGORA COM POUCA

NOW WITH

LITTLE

DIFICUL

DIFFICULT

252

COMPUTER OUTPUT FROM PROGRAMMED

GRAMMAR

ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) MESMO QUE QUASE TODOS OS HOMENS ANDEM AGORA COM POUCA DIFICULDADE . ENG. HE WILL WORK EVEN THOUGH ALMOST ALL THE MEN WALK NOW W ITH LITTLE DIFFICULTY . CONDITION KERNEL POR. QUASE TODOS OS HOMENS ANDAR(AO AGORA COM POUCA OIFICUL DADE . ENG

ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT ADDITIONAL KERNEL

POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) CASO QUASE TODOS OS HOMENS ANDEM AGOR A COM POUCA DIFICULDADE . ENG- HE WILL WORK IN CASE ALMOST ALL THE MEN WALK NOW WITH LITTLE DIFFICULTY . PURPOSE KERNEL POR. QUASE TODOS OS HOMENS ANDAR1AO AGORA COM POUCA DIFICUL DADE . ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y . ADDITIONAL KERNEL POR. E-LE TRABALHARA).

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

253

ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) PARA QUE QUASE TODOS OS HOMENS ANDEM AGORA COM POUCA DIFICULDADE . ENG. HE WILL WORK IN ORDER THAT ALMOST ALL THE MEN WALK NOW WITH LITTLE DIFFICULTY . NEGATIVE RESULT KERNEL POR. QUASE TODOS OS HOMENS ANDARtAO AGORA COM POUCA OIFICUL DADE ENG. ALMOST ALL THE MEN MILL WALK NOW WITH LITTLE DIFFICULT Y . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SEM QUE QUASE TODOS OS HOMENS ANDEM A GORA COM POUCA DIFICULDADE . ENG. HE WILL WORK WITHOUT ALMOST ALL THE MEN )S WALKING NOW WITH LITTLE DIFFICULTY . PREDICTIVE IF KERNEL POR. QUASE TODOS OS HOMENS ANDARiAO AGORA COM POUCA OIFICUL DADE . ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y . ADDITIONAL KERNEL POR. E=LE TRABALHARA)» ENG. HE WILL WORK. TRANSFORMED KERNEL

254

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. E=LE TRABALHARA) SE QUASE TODDS OS HOMENS ANDAREM AGOR A COM POUCA DIFICULDADE . ENG. HE WILL WORK IF ALMOST ALL THE MEN WALK NOW WITH LITTL E DIFFICULTY . CONTRARY IF KERNEL POR. QUASE TOOOS OS HOMENS ANDAR(AO AGORA COM POUCA DIFICUL DADE . ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y . ADDITIONAL KERNEL POR, EU 0 FAREI. ENG. I WILL DO IT. TRANSFORMED KERNEL POR. EU 0 FARIA SE-QUASE TODOS OS HOMENS ANDASSEM AGORA COM POUCA DIFICULDADE . ENG. I WOULD DO IT IF ALMOST ALL THE MEN WALKED NOW WITH LI 7TLE DIFFICULTY . FALSE MANNER KERNEL POR. QUASE TODOS OS HOMENS ANDAR(AO AGORA COM POUCA DIFICUL DADE . ENG. ALMOST ALL THE MEN WILL WALK NOW WITH LITTLE DIFFICULT Y . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) COMO SE QUASE TODOS OS HOMENS ANDASSE M AGORA COM POUCA DIFICULDADE .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

255

ENG. HE WILL WORK AS IF ALMOST ALL THE MEN WALKED NOW WITH LITTLE DIFFICULTY .

ITERATION

25 KERNEL

POR. DUAS MULHERES PODEM VI VER . ENG. TWO WOMEN ARE ABLE TO LIVE . ONE STRING

TRANSFORMATIONS

COMPOUND POR. DUAS MULHERES E A MULHER PODEM VIVER . ENG. TWO WOMEN AND THE WOMAN ARE ABLE TO LIVE . ALTERNATE POR. DUAS MULHERES O U A MULHER PODEM VIVER . ENG. TWO WOMEN OR T H E WOMAN ARE ABLE TO LIVE . NEGATIVE POR. DUAS MULHERES OU A MULHER N(AO PODEM VIVER . ENG. TWO WOMEN OR THE WOMAN ARE NOT ABLE TO LIVE . INTERROGATIVE POR. QUEM NlAO PODE VIVER .Q ENG. WHO IS NOT ABLE TO LIVE .Q T W O STRING TRANSFORMATIONS MODIFICATION KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE T O LIVE . DERIVED KERNEL

256

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. 0 BRASIL ESTA) ARRANHANDO DUAS MULHERES . ENG. BRAZIL IS SCRATCHING TWO WOMEN . TRANSFORMED KERNEL POR. DUAS MULHERES QUE 0 BRASIL ESTA) ARRANHANDO PODEM VIVE R . ENG. TWO WOMEN WHOM BRAZIL IS SCRATCHING ARE ABLE TO LIVE . SENTENCE AS NOUN PHRASE KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . KERNEL CONTAINING NOUN PHRASE POR. E) P O S S D V E L ENG. IT IS POSSIBLE. TRANSFORMED KERNEL POR. E) P O S S D V E L QUE DUAS MULHERES POSSAM VIVER . ENG. IT IS POSSIBLE FOR TWO WOMEN TO BE ABLE TO LIVE . SENTENCE AS TIME ELEMENT KERNEL POR. DUAS MULHERES PODEM VIVER , ENG. TWO WOMEN ARE ABLE TO LIVE . KERNEL CONTAINING TIME ELEMENT POR. PAULO ESTA) AQUI AGORA. ENG. PAUL IS HERE NOW. TRANSFORMED KERNEL POR. PAULO ESTA) AQUI QUANDO DUAS MULHERES PODEM VIVER . ENG. PAUL IS HERE WHEN TWO WOMEN ARE ABLE TO LIVE .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

257

SENTENCE AS PLACE ELEMENT KERNEL POft. OUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . KERNEL CONTAINING PLACE ELEMENT POR. JOCAO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JO(AO COME ONDE DUAS MULHERES PODEM VIVER . ENG. JOHN EATS WHERE TWO WOMEN ARE ABLE TO LIVE . SENTENCE AS MANNER ELEMENT KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . DERIVED KERNEL POR. AS MO=C)AS TEEM ESTADO VIVENDO . ENG. THE GIRLS HAVE BEEN LIVING . TRANSFORMED KERNEL POR. DUAS MULHERES PODEM VIVER COMO AS MO=C)AS TEEM ESTADO VIVENDO . ENG. TWO WOMEN ARE ABLE TO LIVE LIKE THE GIRLS HAVE BEEN LI VING . OPTIONAL ONE STRING TRANSFORMATION POR. DUAS MULHERES PODEM VIVER COMO AS MO=C)AS . ENG. TWO WOMEN ARE ABLE TO LIVE LIKE THE GIRLS . INTERROGATIVE AS NOUN PHASE INTERROGATIVE

258

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. QUEM N(AO PODE VIVER .Q ENG. WHO IS NOT ABLE TO LIVE .Q KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI QUEM N(AO PODE VIVER . ENG. I KNOW WHO IS NOT ABLE TO LIVE . CONCESSION KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E=LE TRABALHA MESMO QUE DUAS MULHERES POSSAM VIVER . ENG. HE WORKS EVEN THOUGH TWO WOMEN ARE ABLE TO LIVE . CONDITION KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. E=LE TRABALHA CASO OUAS MULHERES POSSAM VIVER . ENG. HE WORKS IN CASE TWO WOMEN ARE ABLE TO LIVE . PURPOSE KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E= LE TRABALHA PARA QUE DUAS MULHERES POSSAM VIVER . ENG. HE WORKS IN ORDER THAT TWO WOMEN CAN LIVE . NEGATIVE RESULT KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E=LE TRABALHA SEM QUE DUAS MULHERES POSSAM VIVER . ENG. HE WORKS WITHOUT TWO WOMEN )S BEING ABLE TO LIVE . PREDICTIVE IF KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE .

259

260

COMPUTET! OCT PUT FROM PROGRAMMED GRAMMAR

ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SE DUAS MULHERES PUDEREM VIVER . ENG. HE WILL WORK IF TWO WOMEN ARE ABLE TO LIVE . CONTRARY IF KERNEL POR. OUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . ADDITIONAL KERNEL POR. EU 0 FAREI. ENG. I WILL DO IT. TRANSFORMED KERNEL POR. EU 0 FARIA SE DUAS MULHERES PUDESSEM VIVER . ENG. I WOULD DO IT IF TWO WOMEN WERE ABLE TO LIVE . FALSE MANNER KERNEL POR. DUAS MULHERES PODEM VIVER . ENG. TWO WOMEN ARE ABLE TO LIVE . ADDITIONAL KERNEL eOfc. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POJV. £*LE TRABALHA COMO SE DUAS MULHERES PUDESSEM VIVER . E^G,. HE WORKS AS IF TWO WOMEN WERE ABLE TO LIVE .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ITERATION

261

26 KERNEL

POR. TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . ONE STRING TRANSFORMATIONS COMPOUND POR. TU E NO)S TI)NHAMOS ESTADO CONTANDO COM UMA CASA . ENG. YOU AND WE HAD BEEN COUNTING ON ONE HOUSE . ALTERNATE POR. TU OU NO)S TI)NHAMOS ESTADO CONTANDO COM UMA CASA . ENG. YOU OR WE HAD BEEN COUNTING ON ONE HOUSE . NEGATIVE POR. TU OU NO)S N(AO TI)NHAMOS ESTADO CONTANDO COM UMA CASA ENG. YOU OR WE HAD NOT BEEN COUNTING ON ONE HOUSE . INTERROGATIVE POR. POR QUE TU OU NO)S N(AO TI)NHAMOS ESTADO CONTANDO COM UMA CASA .Q ENG. WHY HAD YOU OR WE NOT BEEN COUNTING ON ONE HOUSE .Q TWO STRING TRANSFORMATIONS MODIFICATION KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . DERIVED KERNEL

262

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. UMA CASA DETERIORA . ENG. ONE HOUSE DETERIORATES . TRANSFORMED KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA QUE DETERIORA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE WHICH DETERIORATES SENTENCE AS NOUN PHRASE KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . KERNEL CONTAINING NOUN PHRASE POR. EU TEMIA ISSO. ENG. I FEARED THAT. TRANSFORMED KERNEL POR. EU TEMIA QUE TU TIVESSES ESTADO CONTANDO COM UMA CASA ENG. I FEARED THAT YOU HAD BEEN COUNTING ON ONE HOUSE . SENTENCE AS TIME ELEMENT KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . KERNEL CONTAINING TIME ELEMENT POR. EU ESTIVE LA) ONTEM. ENG. I WAS THERE YESTERDAY. TRANSFORMED KERNEL POR. EU ESTIVE LA) QUANDO TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. I WAS THERE WHEN YOU HAD BEEN COUNTING ON ONE HOUSE .

263

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

SENTENCE

AS PLACE ELEMENT

KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA ENG. YOU HAD BEEN COUNTING CN ONE HOUSE KERNEL CONTAINING PLACE

.

.

ELEMENT

POR. JO(AO COME A Q U I . ENG. JOHN EATS

HERE.

TRANSFORMED KERNEL POR. JO(AO COME ONDE TU TINHAS ESTADO CONTANDO COM UMA CASA ENG. JOHN EATS WHERE YOU HAD BEEN COUNTING ON ONE HOUSE

.

SENTENCE AS MANNER ELEMENT KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA ENG. YOU HAD BEEN COUNTING ON ONE HOUSE

.

.

DERIVED KERNEL POR.

A MCMOA CONTA COM UMA CASA

ENG. THE GIRL COUNTS ON ONE HOUSE

. .

TRANSFORMED KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA CQMO A MO=C)A C ONTA COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE L I K E THE GIRL COUNT S ON ONE HOUSE . OPTIONAL

ONE STRING TRANSFORMATION

POR. TU TINHAS ESTADO CONTANDO COM UMA CASA COMO A MO=C)A ENG. YOU HAD BEEN COUNTING ON ONE HOUSE L I K E THE GIRL INTERROGATIVE AS NOUN PHASE

.

.

264

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

INTERROGATIVE POR.

POR QUE TU OU N O ) S N ( A O T D N H A M O S UMA C A S A . Q

ESTADO CONTANDQ COM

E N G . WHY HAD YOU OR WE NOT B E E N C O U N T I N G ON ONE HOUSE

.Q

K E R N E L C O N T A I N I N G NOUN P H R A S E POR.

EU S E I

ENG.

I

A

VEROADE.

KNOW THE

TRUTH.

TRANSFORMED

KERNEL

POR.

EU S E I POR QUE TU OU N O ) S N (AO T D N H A M O S DO COM UMA C A S A .

ESTADO

CONTAN

ENG.

I KNOW WHY YOU OR WE HAD NOT B E E N COUNTING ON ONE HOUS E . CONCESSION KERNEL

POR.

TU T I N H A S

ESTADO CONTANDO COM UMA CASA

ENG.

YOU HAD B E E N C O U N T I N G ADDITIONAL

POR.

E=LE

E N G . HE

ON ONE HOUSE

.

.

KERNEL

TRABALHOU.

WORKED. TRANSFORMED

KERNEL

POR.

E = L E T R A B A L H O U MESMO QUE T U T I V E S S E S OM UMA C A S A .

ESTADO CONTANDO C

ENG.

HE WORKED E V E N THOUGH YOU HAD B E E N C O U N T I N G ON ONE HOU SE . CONDITION KERNEL

P O R . TU T I N H A S ENG.

ESTADO CONTANDO COM UMA CASA

YOU HAD B E E N C O U N T I N G ON ONE HOUSE ADDITIONAL

KERNEL

.

.

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

265

POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU CASO TU TIVESSES ESTADO CONTANDO COM UM A CASA . ENG. HE WORKED IN CASE YOU HAD BEEN COUNTING ON ONE HOUSE . PURPOSE KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU PARA QUE TU CONTASSES COM UMA CASA • ENG. HE WORKED IN ORDER THAT YOU COUNT Q N ONE HOUSE • NEGATIVE RESULT KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU SEM QUE TU CONTASSES COM UMA CASA ENG. HE WORKED WITHOUT YOUR COUNTING ON ONE HOUSE .

266

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

CONTRARY IF KERNEL POR. TU TINHAS ESTADO CONTANGO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . ADDITIONAL KERNEL POR. EU IA FAZE=-LO. ENG. I WAS GOING TO DO IT. TRANSFORMED KERNEL POR. EU O TERIA FEITO SE TU TIVESSES ESTADO CONTANDO COM UM A CASA . ENG. I WOULD HAVE DONE IT IF YOU HAD BEEN COUNTING ON ONE H OUSE . FALSE MANNER KERNEL POR. TU TINHAS ESTADO CONTANDO COM UMA CASA . ENG. YOU HAD BEEN COUNTING ON ONE HOUSE . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU COMO SE TU TIVESSES ESTADO CONTANDO COM UMA CASA . ENG. HE WORKED AS IF YOU HAD BEEN COUNTING ON ONE HOUSE .

ITERATION

27 KERNEL

POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE .

COMPUTER O U i T U T FROM PROGRAMMED

GRAMMAR

267

ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . ONE STRING TRANSFORMATIONS PASSIVE POR. QUALQUER OUTRO LIVRO SERA) PROMETIDO PARA VOSSAS OUTRA S COBRAS POR MIM CADA DOIS DIAS COM MUITA DIFICULDADE • ENG. ANY OTHER BOOK WILL BE PROMISED TO YOUR OTHER SNAKES B Y ME EVERY TWO DAYS WITH MUCH DIFFICULTY . COMPOUND POR. QUALQUER OUTRO LIVRO E ESTAS UJLTIMAS VINTE ARRAS SER( AO PROMETIDOS PARA VOSSAS OUTRAS COBRAS POR MIM CADA D OIS DIAS COM MUITA DIFICULDADE . ENG. ANY OTHER BOOK AND THESE LAST TWENTY BANNS WILL BE PRO MISED TO YOUR OTHER SNAKES BY ME EVERY TWO DAYS WITH M UCH DIFFICULTY . ALTERNATE POR. QUALQUER OUTRO LIVRO OU ESTAS U R T I M A S VINTE ARRAS SER (AO PROMETIDOS PARA VOSSAS OUTRAS COBRAS POR MIM CADA DOIS DIAS COM MUITA DIFICULDADE ENG. ANY OTHER BOOK OR THESE LAST TWENTY 8ANNS WILL BE PROM ISED TO YOUR OTHER SNAKES BY ME EVERY TWO DAYS WITH MU CH DIFFICULTY . NEGATIVE POR. NENHUM OUTRO LIVRO SERA) PROMETIDO PARA VOSSAS OUTRAS COBRAS POR MIM CADA DOIS OIAS COM MUITA DIFICULDADE NE M FA(CILMENTE . ENG. NOT ANOTHER BOOK WILL BE PROMISED TO YOUR OTHER SNAKES BY ME EVERY TWO DAYS WITH MUCH DIFFICULTY NOR EASILY INTERROGATIVE POR. QUANDO NENHUM OUTRO LIVRO SERA) PROMETIDO PARA VOSSAS OUTRAS COBRAS POR MIM COM MUITA DIFICULDADE NEM FAICIL MENTE .Q ENG. WHEN WILL NOT ANOTHER BOOK BE PROMISED TO YOUR OTHER S

268

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

NAKES BY ME WITH MUCH DIFFICULTY NOR EASILY .Q TWO STRING TRANSFORMATIONS MODIFICATION KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . DERIVED KERNEL POR. QUALQUER OUTRO LIVRO TEM ESTADO SUSTENTANDO ALGO . ENG. ANY OTHER BOOK HAS BEEN SUPPORTING SOMETHING . TRANSFORMED KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO QUE TEM EST ADO SUSTENTANDO ALGO PARA VOSSAS OUTRAS COBRAS CADA DO IS DIAS COM MUITA DIFICULDADE . ENG. I WILL BE PROMISING ANY OTHER BOOK WHICH HAS BEEN SUPP ORTING SOMETHING TO YOUR OTHER SNAKES EVERY TWO DAYS W ITH MUCH DIFFICULTY . SENTENCE AS NOUN PHRASE KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDAOE . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH WJCH DIFFICULTY . KERNEL CONTAINING NOUN PHRASE POR. ISSO ME INTERESSA. ENG. THAT INTERESTS ME. TRANSFORMED KERNEL POR. INTERESSA-ME QUE EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUI TA DIFICULDADE .

COMPUTER OUTPUT FROM P R O G R A M M E D G R A M M A R

269

ENG. IT INTERESTS ME THAT I WILL BE PROMISING ANY OTHER BOO K TO YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFIC ULTY . SENTENCE AS TIME ELEMENT KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . KERNEL CONTAINING TIME ELEMENT POR. MARIA ESTARA) AQUI AMANHiA. ENG. MARY WILL BE HERE TOMORROW. TRANSFORMED KERNEL POR. MARIA ESTARA) AQUI QUANDO EU ESTIVER PROMETENDO QUALQU ER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIA S COM MUITA DIFICULDADE . ENG. MARY WILL BE HERE WHEN I AM PROMISING ANY OTHER BOOK T 0 YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULT Y . SENTENCE AS PLACE ELEMENT KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . KERNEL CONTAINING PLACE ELEMENT POR. JOIAQ COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JO IAO COME ONDE EU ESTAREI PROMETENDO QUALQUER OUTRO L IVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUIT A DIFICULDADE -

270

COMPUTER OUTPUT FROM P R O G R A M M E D G R A M M A R

E N G . JOHN EATS WHERE I WILL BE PROMISING ANY OTHER BOOK T O YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULTY SENTENCE

AS MANNER

ELEMENT

KERNEL POR. EU ESTAREI PROMETENDO QUALQUER O U T R O , L I V R O PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA D I F I C U L D A D E . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER S EVERY TWO D A Y S WITH MUCH DIFFICULTY . DERIVED

SNAKE

KERNEL

POR. A MO=C)A PROMETE QUALQUER OUTRO LIVRO . E N G . THE GIRL PROMISES ANY OTHER BOOK . TRANSFORMED

KERNEL

P O R . EU ESTAREI PROMETENDO QUALQUER OUTRO L I V R O PARA VOSSAS OUTRAS COBRAS CAOA DOIS DIAS COM MUITA D I F I C U L D A D E CO MO A MO=C)A PROMETE QUALQUER OUTRO LIVRO . E N G . I WILL BE PROMISING ANY OTHER BOOK T O YOUR OTHER SNAKE S EVERY TWO D A Y S WITH MUCH DIFFICULTY L I K E THE G I R L PR OMISES ANY OTHER BOOK . OPTIONAL ONE STRING

TRANSFORMATION

POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS C O B R A S CADA DOIS DIAS COM MUITA DIFICULDADE CO MO A MO=C)A . E N G . I WILL B E PROMISING ANY OTHER BOOK TO YOUR OTHER S N A K E S EVERY TWO DAYS WITH MUCH DIFFICULTY L I K E THE GIRL . INTERROGATIVE A S NOUN

PHASE

INTERROGATIVE POR. QUANDO NENHUM OUTRO LIVRO SERA) PROMETIDO PARA V O S S A S OUTRAS C O B R A S POR MIM COM MUITA D I F I C U L D A D E NEM FAICIL MENTE .Q ENG. WHEN W I L L NOT ANOTHER BOOK BE PROMISED TO YOUR OTHER NAKES BY ME W I T H MUCH DIFFICULTY NOR EASILY .Q KERNEL C O N T A I N I N G NOUN

PHRASE

S

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

271

POR. EU SEI A VERDADE. ENG. I KNGW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI QUANDO NENHUM OUTRD LIVRO SERA) PROMETIDO PARA VDSSAS OUTRAS COBRAS. POR HIM COM MUITA DIFICULDAOE NEM FA(CILMENTE . ENG. I KNOW WHEN NOT ANOTHER BOOK WILL BE PROMISED TO YOUR OTHER SNAKES BY ME WITH MUCH DIFFICULTY NOR EASILY .

CONCESSION KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDAOE . ENG. 3 WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . ADDITIONAL KERNEL POR.. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) MESMO QUE EU ESTEJA PROMETENDO QUALQU ER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIA S COM MUITA DIFICULDADE . ENG. HE WILL WORK EVEN THOUGH I AM PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICU LTY . CONDITION KE-RNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . ADDITIONAL KERNEL

272

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. E=LE TRABALHARA}.

ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) CASO EU ESTEJA PROMETENDO QUALQUER OU TRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. HE WILL WORK IN CASE I AM PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULTY PURPOSE KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA ODIS DIAS COM MUITA DIFICULDADE . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK* TRANSFORMED KERNEL POR. E=LE TRABALHARA) PARA QUE EU PROMETA QUALQUER OUTRO LI VRO PARA.VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. HE WILL WORK IN ORDER THAT I PROMISE ANY OTHER BOOK TO YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULTY • NEGATIVE RESULT KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . ADDITIONAL KERNEL

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

273

POR. E=LE TRABALHARA). ENG. HE MILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SEM QUE EU PROMETA NENHUM OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DI FICULDADE . ENG. HE WILL WORK WITHOUT M Y PROMISING ANY OTHER BOOK TO YO UR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULTY . PREDICTIVE IF KERNEL POR. EU.ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . ADDITIONAL KERNEL POR. E=LE TRABALHARA»* ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SE EU ESTIVER PROMETENDO QUALQUER OUT RO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE * ENG. HE WILL WORK IF I AM PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULTY . CONTRARY IF KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CAOA DOIS DIAS COM MUITA DIFICULDADE * ENG. I WILL BE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO DAYS WITH MUCH DIFFICULTY . ADDITIONAL KERNEL POR. EU 0 FAREI.

274

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. I WILL DO IT. TRANSFORMED KERNEL POR. EU 0. PARI A SE EU ESTIVESSE PROMETENDO QUALQUER OUTRO L IVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM. MUIT A DIFICULDADE . ENG. I WOULD DO IT IF I WERE PROMISING ANY OTHER BOOK TO YO OR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULTY . FALSE MANNER KERNEL POR. EU ESTAREI PROMETENDO QUALQUER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DIAS COM MUITA DIFICULDADE . ENG. I WILL B E PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKE S EVERY TWO" DAYS WITH MUCH DIFFICULTY . ADDITIONAL KERNEL POR« E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) COMO SE EU ESTIVESSE PROMETENDO QUALQ UER OUTRO LIVRO PARA VOSSAS OUTRAS COBRAS CADA DOIS DI AS COM MUITA DIFICULDADE . ENG. HE WILL WORK AS IF I WERE PROMISING ANY OTHER BOOK TO YOUR OTHER SNAKES EVERY TWO DAYS WITH MUCH DIFFICULTY •

ITERATION

28 KERNEL

POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . ONE STRING TRANSFORMATIONS COMPOUND

COMPUTER OUTPUT PROM PROGRAMMED GRAMMAR

275

POR. QUASE TO=DA A OUTRA MULHER E VINTE MULHERES TER(AO EST ADO MORRENDO . ENG- ALMOST ALL THE OTHER WOMAN AND TWENTY WOMEN WILL HAVE BEEN OYING . ALTERNATE POR. QUASE TO=DA A OUTRA MULHER OU VINTE MULHERES TERCAO ES TADO MORRENDO • ENG. ALMOST ALL THE OTHER WOMAN OR TWENTY WOMEN WILL HAVE B EEN DYING . NEGATIVE POR. QUASE TO=DA A OUTRA MULHER OU VINTE MULHERES N(A0 TER( AO ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN OR TWENTY WOMEN WILL NOT HA VE BEEN DYING . INTERROGATIVE POR. QUEM N(AO TERA) ESTADO MORRENDO .Q ENG. WHO WILL NOT HAVE BEEN DYING .Q TWO STRING TRANSFORMATIONS MODIFICATION KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . DERIVED KERNEL POR. DOIS HOMENS ME EST(AO PROMETENDO PARA QUASE TO=DA A OU TRA MULHER . ENG. TWO MEN ARE PROMISING ME TO ALMOST ALL THE OTHER WOMAN • TRANSFORMED KERNEL POR. QUASE TO=DA A OUTRA MULHER PARA QUEM DOIS HOMENS ME ES T(AO PROMETENDO TERA) ESTADO MORRENDO .

276

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. ALMOST ALL THE OTHER WOMAN TO WHOM TWO MEN ARE PROM I SI NG ME WILL HAVE BEEN DYING . SENTENCE AS NOUN PHRASE KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI QUE QUASE TO=DA A OUTRA MULHER TERA) ESTADO MOR RENDO . ENG. I KNOW THAT ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . SENTENCE AS TIME ELEMENT KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . KERNEL CONTAINING TIME ELEMENT POR. MARIA ESTARA) AQUI AMANHtA. ENG. MARY WILL BE HERE TOMORROW. TRANSFORMED KERNEL POR. MARIA ESTARA) AQUI QUANDO QUASE TO=DA A OUTRA MULHER T IVER ESTADO MORRENDO . ENG. MARY WILL BE HERE WHEN ALMOST ALL T H E OTHER .WOMAN HAS BEEN DYING . SENTENCE AS PLACE ELEMENT KERNEL POR. QUASE TO=DA A OUTRA MULHER TERÄi ESTADO MORRENDQ

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

277

ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . KERNEL CONTAINING PLACE ELEMENT POR. JO(AO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JO(AO COME ONDE QUASE TO=DA A OUTRA MULHER TERA) ESTAD 0 MORRENDO . ENG. JOHN EATS WHERE ALMOST ALL THE OTHER WOMAN WILL HAVE B EEN DYING . SENTENCE AS MANNER ELEMENT KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . DERIVED KERNEL POR. A HO=C)A MORRE . ENG. THE GIRL DIES . TRANSFORMED KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO COMÖ A MO=C)A MORRE . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING LIKE T HE GIRL DIES . OPTIONAL ONE STRING TRANSFORMATION POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO COMO A MO=C)A . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING LIKE T HE GIRL . INTERROGATIVE AS NOUN PHASE INTERROGATIVE POR. QUEM N(AO TERA) ESTADO MORRENDO .Q

278

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

ENG. WHO WILL NOT HAVE BEEN DYING .Q KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDAOE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI QUEM N(AO TERA) ESTADO MORRENDO . ENG. I KNOW WHO WILL NOT HAVE BEEN DYING . CONCESSION KERNEL POR. QUASE TO=D A A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) MESMO QUE QUASE TO=DA A OUTRA MULHER TENHA ESTADO MORRENDO . ENG. HE WILL WORK EVEN THOUGH ALMOST ALL THE OTHER WOMAN HA S BEEN DYING . CONDITION KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

279

POR. E=LE TRABALHARA) CASO QUASE TO=DA A QUTRA MULHER TENHA ESTADO MORRENDO . ENG. HE WILL WORK IN CASE ALMOST ALL THE OTHER WOMAN HAS BE EN DYING . PURPOSE KERNEL POR. QUASE TO=D A A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED

KERNEL

POR. E=LE TRABALHARA) PARA QUE QUASE TO=DA A OUTRA MULHER M ORRA . ENG. HE WILL WORK IN ORDER THAT ALMOST ALL THE OTHER WOMAN DIE . NEGATIVE RESULT KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED

KERNEL

POR. E=LE TRABALHARA) SEM QUE QUASE TO=DA A OUTRA MULHER MO RR A . ENG. HE WILL WORK WITHOUT ALMOST ALL THE OTHER WOMAN )S DYI NG . PREDICTIVE IF

280

COMPUTER

OUTPUT FROM PROGRAMMED

GRAMMAR

KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . •ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WIL1 WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SE QUASE TO=OA A OUTRA MULHER TIVER E STADO MORRENDO . ENG. HE WILL WORK IF ALMOST ALL THE OTHER WOMAN HAS BEEN DY ING . CONTRARY IF KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL HAVE BEEN DYING . ADDITIONAL KERNEL POR. EU 0 FAREI. ENG. I WILL DO IT. TRANSFORMED KERNEL POR. EU 0 FARIA SE QUASE T0=DA A OUTRA MULHER TIVESSE ESTAD 0 MORRENDO . ENG. I WOULD DO IT IF ALMOST ALL THE OTHER WOMAN HAD BEEN D YING . FALSE MANNER KERNEL POR. QUASE TO=DA A OUTRA MULHER TERA) ESTADO MORRENDO . ENG. ALMOST ALL THE OTHER WOMAN WILL. HAVE BEEN DYING . ADDITIONAL KERNEL

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

281

POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) COMO SE QUASE TO=DA A OUTRA MULHER TI VESSE ESTADO MORRENDO . ENG. HE WILL WORK AS IF ALMOST ALL THE OTHER WOMAN HAD BEEN DYING .

ITERATION

29 KERNEL

POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDEJIAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . ONE STRING TRANSFORMATIONS COMPOUND POR. QUASE OUTROS VINTE AMORES E UM OUTRO ME=DO CONSISTIRAM EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES AND ANOTHER FEAR CONSISTED 0 F IDEAS WELL . ALTERNATE POR. QUASE OUTROS VINTE AMORES OU UM OUTRO ME=DQ CONSISTIRA M EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES OR ANOTHER FEAR CONSISTED OF IDEAS WELL . NEGATIVE POR. QUASE OUTROS VINTE AMORES OU UM OUTRO ME=DO N(AO CONSI STIRAM EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES OR ANOTHER FEAR DID NOT CONS SI ST OF IDEAS WELL . INTERROGATIVE

282

COMPUTER OUTPUT FROM PROGRAMMED

GRAMMAR

POR. COMO QUASE OUTROS VINTE AMORES OU UM OUTRO ME=DO NIAO CONSISTIRAM EM IDE)IAS .Q ENG. HOW DID ALMOST TWENTY OTHER LOVES OR ANOTHER FEAR NOT CONSIST OF IDEAS .Q TWO STRING TRANSFORMATIONS MODIFICATION KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . DERIVED KERNEL POR. A SENHORA PROMETE IDE)IAS PARA ESSA COBRA . ENG. YOU PROMISE IDEAS TO THAT SNAKE . TRANSFORMED KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS QUE A SENHORA PROMETE PARA ESSA COBRA BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS THAT YOU PROMISE TO THAT SNAKE WELL . SENTENCE AS NOUN PHRASE KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL POR. EU SEI QUE QUASE OUTROS VINTE AMORES CONSISTIRAM EM ID E)IAS BEM . ENG. I KNOW THAT ALMOST TWENTY OTHER LOVES CONSISTED OF IDE AS WELL .

COMPUTER

OUTPUT FROM PROGRAMMED GRAMMAR

283

SENTENCE AS TIME ELEMENT KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . KERNEL CONTAINING TIME ELEMENT POR. EU ESTIVE LA) ONTEM. ENG. I WAS THERE YESTERDAY. TRANSFORMED KERNEL POR. EU ESTIVE LA) QUANDO QUASE OUTROS VINTE AMORES CONSIST IRAM EM IDE)IAS BEM . ENG. I WAS THERE WHEN ALMOST TWENTY OTHER LOVES CONSISTED 0 F IDEAS WELL . SENTENCE AS PLACE ELEMENT KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . KERNEL CONTAINING PLACE ELEMENT POR. JO(AO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JO(AO COME ONDE QUASE OUTROS VINTE AMORES CONSISTIRAM EM I D E H A S BEM . ENG. JOHN EATS WHERE ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . SENTENCE AS MANNER ELEMENT KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDEJIAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL .

284

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

DERIVED KERNEL PGR. OS SONHOS EST(AD CONSISTINDO EM I DE)IAS . ENG. THE DREAMS ARE CONSISTING OF IDEAS . TRANSFORMED KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM C OMO OS SONHOS EST(AO CONSISTINDO EM IDEJIAS . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL LIKE THE DREAMS ARE CONSISTING OF IDEAS . OPTIONAL ONE STRING TRANSFORMATION POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM C OMO OS SONHOS . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL LIKE THE DREAMS . INTERROGATIVE AS NOUN PHASE INTERROGATIVE POR. COMO QUASE OUTROS VINTE AMORES OU UM OUTRO ME=DO NiAO CONSISTIRAM EM I D E H A S .Q ENG. HOW DID ALMOST TWENTY OTHER LOVES OR ANOTHER FEAR NOT CONSIST OF IDEAS .Q KERNEL CONTAINING NOUN PHRASE POR. EU SEI A VERDADE. ENG. I KNOW THE TRUTH. TRANSFORMED KERNEL PGR. EU SEI COMO QUASE OUTROS VINTE AMORES OU UM OUTRO ME=D O N(AO CONSISTIRAM EM IDE)IAS . ENG. I KNOW HOW ALMOST TWENTY OTHER LOVES OR ANOTHER FEAR D ID NOT CONSIST OF IDEAS . CONCESSION KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

285

ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU MESMO QUE QUASE OUTROS VINTE AMORES CON SISTISSEM EM IDEJIAS BEM . ENG. HE WORKED EVEN THOUGH ALMOST TWENTY OTHER LOVES CONSIS TED OF IDEAS WELL . CONDITION KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)I AS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU CASO QUASE OUTROS VINTE AMORES CONSISTI SSEM EM IDE1) I AS BEM . ENG. HE WORKED IN CASE ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . PURPOSE KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDEJIAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED.

286

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

TRANSFORMED KERNEL POR. E=LE TRABALHOU PARA QUE QUASE OUTROS VINTE AMORES CONS ISTISSEM EM I D E H A S BEM . ENG. HE WORKED IN ORDER THAT ALMOST TWENTY OTHER LOVES CONS 1ST OF IDEAS WELL . NEGATIVE RESULT KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM I D E H A S BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU SEM QUE QUASE OUTROS VINTE AMORES CONSI STISSEM EM IDE)I AS BEM . ENG. HE WORKED WITHOUT ALMOST TWENTY OTHER LOVES ) CONSISTI NG OF IDEAS WELL . CONTRARY IF KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM I D E H A S BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . ADDITIONAL KERNEL PORi EU IA FAZE^-LO. ENG. I WAS GOING TO DO IT. TRANSFORMED KERNEL POR. EU 0 TERIA FEITO SE QUASE OUTROS VINTE AMORES TIVESSEM CONSI STIDO EM I D E H A S BEM . ENG. I WOULD HAVE DONE IT IF ALMOST TWENTY OTHER LOVES HAD CONSISTED OF IDEAS WELL .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

287

FALSE MANNER KERNEL POR. QUASE OUTROS VINTE AMORES CONSISTIRAM EM IDE)IAS BEM . ENG. ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL . ADDITIONAL KERNEL POR. E=LE TRABALHOU. ENG. HE WORKED. TRANSFORMED KERNEL POR. E=LE TRABALHOU COMO SE QUASE OUTROS VINTE AMORES CONSI STISSEM EM IDE)IAS BEM . ENG. HE WORKED AS IF ALMOST TWENTY OTHER LOVES CONSISTED OF IDEAS WELL .

ITERATION

30 KERNEL

POR. AS SOUSA EST!AO LIMPAS ATRA)S TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . ONE STRING TRANSFORMATIONS COMPOUND POR. AS SOUSA EST(AO QUENTES E LIMPAS ATRA)S TO=DAS AS VINT E CASAS HOJE . ENG. THE SOUSA WOMEN ARE HOT AND CLEAN BEHIND ALL OF THE TW ENTY HOUSES TODAY . ALTERNATE POR. AS SOUSA EST!AO QUENTES OU LIMPAS ATRA)S TO=DAS AS VIN TE CASAS HOJE . ENG. THE SOUSA WOMEN ARE HOT OR CLEAN BEHIND ALL OF THE TWE

288

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

NTY HOUSES TODAY . COMPOUND POR. AS SOUSA EST(AO QUENTES OU LIMPAS ATRAJS T0=DAS AS VIN TE CASAS E LA) HOJE . ENG. THE SOUSA WOMEN ARE HOT OR CLEAN BEHIND ALL OF THE TWE NTY HOUSES AND THERE TODAY . ALTERNATE POR. AS SOUSA E S T U O QUENTES OU LIMPAS ATRAJS TO=DAS AS VIN TE CASAS OU LA) HOJE . ENG. THE SOUSA WOMEN ARE HOT OR CLEAN BEHIND ALL OF THE TWE NTY HOUSES OR THERE TODAY . NEGATIVE POR. AS SOUSA N(AO EST(AO QUENTES NEM LIMPAS ATRA)S TO=DAS AS VINTE CASAS NEM LA) HOJE . ENG. THE SOUSA WOMEN ARE NOT HOT NOR CLEAN BEHIND ALL OF TH E TWENTY HOUSES NOR THERE TODAY . INTERROGATIVE POR. COMO AS SOUSA N(AO E S T U O ATRAJS TO=DAS AS VINTE CASAS NEM LA J HOJE .Q ENG. HOW ARE THE SOUSA WOMEN NOT BEHIND ALL OF THE TWENTY H OUSES NOR THERE TODAY .Q TWO STRING TRANSFORMATIONS MODIFICATION KERNEL POR. AS SOUSA EST{AO LIMPAS ATRAJS TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . DERIVED KERNEL POR. OUTRAS MULHERES OS PROMETEM PARA AS SOUSA . ENG. OTHER WOMEN PROMISE THEM TO THE SOUSA WOMEN .

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

TRANSFORMED

289

KERNEL

PGR.

AS SOUSA P A R A QUEM OUTRAS MULHERES OS PROMETEM L I M P A S A T R A J S TO=DAS AS V I N T E C A S A S H O J E .

£ST(AO

ENG.

THE SOUSA WOMEN TO WHOM OTHER WOMEN PROMISE THEM ARE C L E A N B E H I N D A L L OF THE TWENTY HOUSES TODAY . SENTENCE A S NOUN P H R A S E KERNEL

POR.

AS SOUSA JE .

EST{AO L I M P A S A T R A J S

TO=DAS AS V I N T E C A S A S HO

E N G . THE SOUSA WOMEN A R E C L E A N B E H I N D A L L OF THE SES TODAY KERNEL

C O N T A I N I N G NOUN P H R A S E

P O R . A VERDADE EJ

ISTO.

ENG.

THIS.

THE TRUTH I S

TWENTY HOU

TRANSFORMED

KERNEL

POR.

A VERDADE E J QUE AS SOUSA E S T U J AS V I N T E C A S A S H C J E .

ENG.

THE T R U T H IS THAT THE SOUSA WOMEN ARE C L E A N B E H I N D CF THE TWENTY HOUSES TODAY . SENTENCE A S T I M E

LIMPAS ATRAJS

TO=DAS ALL

ELEMENT

KERNEL PQR.

AS SOUSA JE .

EST tAO L I M P A S A T R A J S TO=DAS AS V I N T E CASAS HO

E N G . THE SOUSA WOMEN A R E C L E A N . B E H I N D SES TODAY . KERNEL POR.

PAULO

E N G . PAUL

ESTAJ IS

CONTAINING TIME AQUI

PAULO

OF THE

TWENTY HOU

ELEMENT

AGORA.

HERE NOW.

TRANSFORMED POR.

ALL

ESTA)

KERNEL

AQUI QUANDO AS SOUSA E S T t A O

LEMPAS

ATRAJS

290

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

T0=DAS AS VINTE CASAS HOJE . ENG. PAUL IS HERE WHEN THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOUSES TODAY . SENTENCE AS PLACE ELEMENT KERNEL POR. AS SOUSA EST(AO LIMPAS ATRA)S TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . KERNEL CONTAINING PLACE ELEMENT POR. JO(AO COME AQUI. ENG. JOHN EATS HERE. TRANSFORMED KERNEL POR. JO IAO COME ONDE AS SOUSA EST(AO LIMPAS ATRAIS TG=DAS A S VINTE CASAS HOJE . ENG. JOHN EATS WHERE THE SOUSA WOMEN ARE CLEAN BEHIND ALL 0 F THE TWENTY HOUSES TODAY . SENTENCE AS MANNER ELEMENT KERNEL POR. AS SOUSA EST(AO LIMPAS ATRA)S TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . DERIVED KERNEL POR. AS MO=CJ AS PODEM ESTAR LIMPAS . ENG. THE GIRLS ARE ABLE TO BE CLEAN . TRANSFORMED KERNEL POR. AS SOUSA EST(AO LIMPAS ATRA1S TO=DAS AS VINTE CASAS HO JE COMO AS MO=C)AS PODEM ESTAR LIMPAS . CJYG. THE .SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY LIKE THE GIRLS ARE ABLE TO BE CLEAN .

291

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

OPTIONAL

ONE S T R I N G

TRANSFORMATION

POR.

AS SOUSA E S T t A O L I M P A S J E COMO AS MO=C)AS .

ENG.

THE SOUSA WOMEN ARE C L E A N B E H I N D ALL SES TODAY L I K E THE G I R L S . INTERROGATIVE

A T R A ) S TO=DAS AS V I N T E C A S A S HO

AS NOUN

OF THE TWENTY HOU

PHASE

INTERROGATIVE POR.

COMO AS SOUSA N ( A O E S T ( A O A T R A ) S TO=DAS AS V I N T E NEM L A ) HOJE . Q

ENG.

HOW ARE THE SOUSA WOMEN NOT OUSES NOR THERE TODAY . Q

BEHIND ALL

OF THE

CASAS

TWENTY H

K E R N E L C O N T A I N I N G NOUN P H R A S E POR.

EU S E I

A

VERDADE.

ENG.

I KNOW THE

TRUTH.

TRANSFORMED

KERNEL

POR.

EU S E I COMO AS SOUSA N I A O E S T ! A O A T R A ) S E C A S A S NEM L A ) H O J E .

TO-DAS

ENG.

I KNOW HOW THE SOUSA WOMEN ARE NOT B E H I N D A L L WENTY HOUSES NOR T H E R E TODAY .

AS

VINT

OF THE T

CONCESSION KERNEL POR.

AS JE

SOUSA .

ESTIAO LIMPAS

ENG.

THE SOUSA WOMEN A R E C L E A N B E H I N D A L L OF THE TWENTY HOU S E S TODAY . ADDITIONAL

POR.

E=LE

ENG.

HE WORKS.

HO

KERNEL

TRABALHA.

TRANSFORMED POR.

A T R A ) S TO=DAS AS V I N T E C A S A S

E=LE TRABALHA

KERNEL

MESMO QUE AS SOUSA

ESTEJAM LIMPAS

ATRA)S

292

COMPUTER, OUTPUT FROM PROGRAMMED GRAMMAR

TO=DAS AS VINTE CASAS HOJE . ENG. HE WORKS EVEN THOUGH THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOUSES TODAY . CONDITION KERNEL POR. AS SOUSA EST(AO LIMPAS ATRAJS TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . ADDITIONAL KERNEL POR. E-LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E=LE TRABALHA CASO AS SOUSA ESTEJAM LIMPAS ATRA)S TO=D AS AS VINTE CASAS HOJE . ENG. HE WORKS IN CASE THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOUSES TODAY . PURPOSE KERNEL POR. AS SOUSA EST(AO LIMPAS ATRAJS TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL POR. E=LE TRABALHA PARA QUE AS SOUSA ESTEJAM LIMPAS ATRAJS TO=DAS AS VINTE CASAS HOJE . ENG. HE WORKS IN ORDER THAT THE SOUSA WOMEN BE CLEAN BEHIND ALL OF THE TWENTY HOUSES TODAY .

COMPUTER

OUTPUT FROM PROGRAMMED

GRAMMAR

293

NEGATIVE RESULT KERNEL POR. AS SOUSA ESTt AO LIMPAS ATRAJS TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . ADDITIONAL KERNEL POR. E=LE TRABALHA. ENG. HE WORKS. TRANSFORMED KERNEL f'OR. E=LE TRABALHA SEM QUE AS SOUSA ESTEJAM LIMPAS ATRAJS T 0=DAS AS VINTE CASAS HOJE . ENG. HE WORKS WITHOUT THE SOUSA WOMEN )S BEING CLEAN BEHIND ALL OF THE TWENTY HOUSES TODAY . PREDICTIVE IF KERNEL POR. AS SOUSA EST(AO LIMPAS ATRAJS TO=DAS AS VINTE CASAS HO JE . ENG. THE SOUSA WOMEN ARE CLEAN BEHIND ALL OF THE TWENTY HOU SES TODAY . ADDITIONAL KERNEL POR. E=LE TRABALHARA). ENG. HE WILL WORK. TRANSFORMED KERNEL POR. E=LE TRABALHARA) SE AS SOUSA ESTIVEREM LIMPAS ATRAJS T 0=DAS AS VINTE CASAS HOJE . ENG. HE WILL WORK IF THE SOUSA WOMEN ARE CLEAN BEHIND ALL 0 F THE TWENTY HOUSES TODAY . CONTRARY IF KERNEL

294

COMPUTER OUTPUT FROM PROGRAMMED GRAMMAR

POR. AS SOUSA EST• rewriting of one string of symbols => rewriting of two strings of symbols [] marks delimiting boundaries of certain constructions; not a concatenation mark : filled by - with principal symbols a designation that the following symbol or symbols (within certain boundaries) ultimately follow the next optional or obligatory element with terminal symbols the designation of a bound morphene () marks to include " P " or " E " , for Portuguese and English; not a concatenation or optional mark when containing " P " or " E " ' with a principal symbol an indication of another occurrence of that symbol in the string or pair of strings Rule Numbers of Several Principal Symbols

Aux 2.379 DO 2.280 Comp 2.246 DP p l i 2.48 DPpI^ 2.49 DP plj ' 2.50 D P . ' 2.43

DPS_ DPSff Sgj DP U 10 NP Loc Man

2.44 2.45 2.40 2.248 2.6 2.444 2.257

Per PN Prod ProNom x Te Tm VP

2.408 2.176 2.240 2.200 2.400 2.486 2.241

Principal Symbols

0 Null 3p Third Person A D J Adjectival Adj Adjective

Adv Adverb Ag Agent Alter Alternate Altern Alternative

432

APPENDIX

Approx Approximator Aux Auxiliary Card Cardinal Conj Conjunction Comp Complement Compar Comparative Compnd Compound Def Definite Article Dem Demonstrative DO Direct Object D P Determiner Phrase (E) English F u t Future Incl Includer Indef Indefinite Article 1 0 Indirect Object Loc Location Man Manner Mkr Marker Mod Modal N Noun Neg Negative Nom Nominal N P Noun Phrase Nuc Nucleus

Ord Ordinal (P) Portuguese P Phrase Per Person Perf Perfect P N Proper Noun Pos Possessive Poss Possessive Pred Predicate Prep Preposition Près Present Prog Progressive ProNom Pronominal Q Interrogative QNP Interrogative Noun Phrase Quan Quantifier Rel Relative S Sentence Sep Separator Subj Subject Suff Suffix Te Tense Tm Time V Verb V P Verb Phrase Superscripts and Subscripts

l p first person 2p second person 3p third person -a- ending in -a-d- ending in -dact activity a d j adjectival adv adverb affir-neg affirmative or negative ag agentive aux auxiliary bis repeated with noun, countable cwith verb, complement

comp a d j complement adjective compar comparative comp prep prepositional complement conj conjunction deg degree det determiner do direct object -e- ending in -e-e- einding in -e-ei- ending in -eiextran extraneous f feminine fam familiar address

433

APPENDIX

for formal address fut future fut sbjc future subjunctive h human -i- ending in -i-i- ending in -i-ie- ending in -ieindef indefinito inf infinitive inh not human int intransitive io indirect object loc location man manner m masculine mod modal mot motive n neuter neg negative -o- ending in -oobj object pap past participle pass passive per permanent perf perfect

ph phrase pi plural plu plural poss possessive prep prepositional pres present pronom pronominal prp present participle prog progressive quan quantity r retort refl reflexive sbjc subjunctive sg singular subj subject S' d0 takes object

Sentence'

direct

S'gubj takes Sentence' as subject tm time tr transitive temp temporary u uncountable un uncountable verb verbal •y- ending in -y-

Symbols Used in Computer Output

) for ' and ' ( for ~ = for ~ .Q for ?

as

BIBLIOGRAPHY

Bach, Emmon 1964 An Introduction to Transformational Grammars (New York: Holt, Rinehart and Winston, Inc.) Barzun, Jacques 1953 "Food for the N. R. F. or 'My God ! What will you have?'", Partisan Review, Vol. XX, No. 6 (November—December, )pp. 660—74. Bull, William E. 1954 "Spanish Adjective Position", Hispania, Vol. XXXVII, No. 1 (March), pp. 32— 38. Chaiyarantana, Chalao 1961 "A Comparative Study of English and Thai Syntax". Unpublished Ph.D. dissertation, Indiana University. Chomsky, Noam 1957 Syntactic Structures (The Hague: Mouton and Company). 1965 Aspects of the Theory of Syntax (Cambridge, Massachusetts: The M.I.T. Press). Contreras, Heles, and Sol Saporta 1960 "The Validation of a Phonological Grammar", Lingua, Vol. IX, No. 1 (March), pp. 1 - 1 5 . Dinneen, David Allen 1962 A Left-to-Rigkt Generative Grammar of French (Cambridge, Massachusetts: Mechanical Translation Group, Research Laboratory of Electronics, Massachusetts Institute of Technology). Firth, J . R. 1956 "Linguistic Analysis and Translation", For Roman Jalcobson, comp. Morris Halle et. al., pp. 133 — 39. (The Hague: Mouton and Company). Garvin, Paul L. 1962 "Computer Participation in Linguistic Research", Language, Vol. 38, No. 4 (October—December), pp. 385 — 89. Goodman, Ralph M., and Robert P. Stockwell n.d. "The Degrees of Grammaticalness" privately circulated paper. Harris, Zellig S. 1957 "Co-occurrence and Transformation in Linguistic Structure", Language, Vol. 33, No. 3 (July—September), pp. 285 — 340. 1954 "Transfer Grammar", International Journal of American Linguistics, Vol. XX, No. 4 (October), pp. 259 — 70. Hill, Archibald A. 1961 "Grammaticality", Word, Vol. 17, No. 1 (April), pp. 1 — 10. 1962 Review of The Grammar of English Nominalizations, by Robert B. Lees, Language, Vol. 38, No. 4 (October—December), pp. 434—44.

436

BIBLIOGRAPHY

Hockett, Charles F . 1961 " G r a m m a r for t h e H e a r e r " , Symposia in Applied Mathematics, Proceedings, Vol. X I I , pp. 220—36 (Providence: American Mathematical Society). Hollander, J o h n 1959 "Versions, Interpretations, Performances", On Translation, ed. Reuben A. Brower (Harvard Studies in Comparative Literature 23) (Cambridge, Massachusetts: H a r v a r d University Press), pp. 205 — 31. Jakobson, R o m a n [1959] "Boas' View of Grammatical Meaning", The Anthropology of Franz Boas: Essays on the Centennial of his Birth, ed. Walter Rochs Goldschmidt ([Menasha, Wisconsin]: American Anthropological Association) pp. 139 — 45. Lamb, Sydney M. 1961 "The Digital Computer as an Aid in Linguistics", Language, Vol. 37, No. 3 (July—September), pp. 382 — 412. Larbaud, Valéry 1946 Sous l'invocation de Saint Jérôme. 5 a edition. ([Paris]: Librairie Gallimard). Lees, R o b e r t B. 1960 The Grammar of English Nominalizations, International Journal of American Linguistics, Vol. X X V I , No. 3, P a r t 2, "Publication 12 of t h e Indiana University Research Center in Anthropology, Folklore, and Linguistics" (Bloomington, Indiana: Indiana University Research Center in Anthropology, Folklore, and Linguistics). Locke, W . N „ and V. H . Yngve 1958 "Research in Translation by Machine a t M.I.T.", Proceedings of the Eighth International Congress of Linguists (Oslo: Oslo University Press), pp. 510— 14. Maclay, Howard, and Mary D. Sleator 1960 "Responses to Language J u d g m e n t s of Grammaticalness", International Journal of American Linguistics, Vol. X X V I , No. 4 (October), pp. 275— 82. Marks, Lawrence E., and George A. Miller 1964 "The Role of Semantic and Syntactic Constraints in the Memorization of English Sentences", Journal of Verbal Learning and Verbal Behavior, Vol. 3, No. 1 (February), pp. 1 — 5. Mehler, Jacques 1963 "Some Effects of Grammatical Transformations on t h e Recall of English Sentences", Journal of Verbal Learning and Verbal Behavior, Vol. 2, No. 4 (November), pp. 3 4 6 - 5 1 . Miller, George A. 1962 "Some Psychological Studies of G r a m m a r " , American Psychologist, Vol. 17, No. 11 (November), pp. 748 — 62. Miller, George A., and Stephen Isard. 1963 "Some Perceptual Consequences of Linguistic Rules", Journal of Verbal Learning and Verbal Behavior, Vol. 2, No. 3 (September), pp. 217 — 28. Moyne, J o h n 1959 "Idiomatic Structures in Machine Translation", General Analysis Technique, Russian—English Research Reports, Part VII, (Georgetown Occasional Papers on Machine Translation, No. 8.) (Washington, D. C.: I n s t i t u t e of Languages and Linguistics, Machine Translation Research Center) Nida, Eugene A. 1959 "Bible Translating", On Translation, E d . Reuben A. Brower (Harvard Studies

BIBLIOGRAPHY

437

in Comparative Literature 23) (Cambridge, Massachusetts: Harvard UniversityPress) pp. 11 — 31. Quine, Williard V. 1969 "Meaning and Translation", On Translation, ed. Reuben A. Brower (Harvard Studies in Comparative Literature 23) (Cambridge, Massachusetts: Harvard University Press) pp. 148 — 72. Richens, R. H., and A. D. Booth 1955 "Some Methods of Mechanized Translation", Machine Translation oj Languages, ed. William N. Locke and A. Donald Booth (New York: The Technology Press of The Massachusetts Institute of Technology and John Wiley and Sons, Inc.) pp. 2 4 - 4 6 . Saporta, Sol, and Heles Contreras 1962 Phonological Grammar of Spanish (Seattle: University of Washington Press) Satterthwait, Arnold Chase 1962 Parallel Sentence Construction Grammars oj Arabic and English (Cambridge, Massachusetts: Mechanical Translation Group, Research Laboratory of Electronics, Massachusetts of Technology). 1965 "Sentence-For-Scntence Translation: An Example", Mechanical Translation, Vol. VIII, No. 2 (February), pp. 1 4 - 3 8 . Schachter, Paul 1959 "A Contrastive Analysis of English and Pangasinan". Unpublished Ph. D. dissertation, The University of California at Los Angeles. Stockwell, Robert P. 1960 "The Place of Intonation in a Generative Grammar of English", Language, Vol. 36, No. 3 (July—September), pp. 360—67. Yngve, V. H. 1957 "A Framework for Mechanical Translation", Mechanical Translation, Vol. IV, No. 3 (December), pp. 59 — 65. 1961 Random Generation oj English Sentences, Memo 1961 — 4. (Cambridge, Massachusetts: Mechanical Translation Group, Research Laboratory of Electronics, Massachusetts Institute of Technology) January 30, 1961; reprinted December, 1964.

INDEX

Applications, practical of this grammar, 25 Arabic, 20 Automatic parsing, 25 Bach, Emmon, 10 Backtracking, 11 Barzun, Jacques, 15 Booth, A. D., fn 14 Bull, William E., 16 Chaiyaratana, Chalao, 19 Chomsky, Noam, 10, 12, 13, 14, 17 COMIT, 23, 24 "Common core" grammar, 18 Computer, 14, 22 Computer output, randomly generated, 7 Computer program, 7, 14, 24—25 Contrastive analysis of English and Pangasinan, 17 Contreras, Heles, 23 Cooccurrence restrictions, 19, 22 Corpus, 8 Dinneen, David Allen, 19 Feedback, 9 Firth, J . R „ 16 Format of this grammar, 7 Forward-moving grammar, 11 French, 19 Garvin, Paul L„ 23 Goodman, Ralph M., 12, 13 Grammar "common core", 18 format, 7 forward-moving, 11 reference, 25 of specifiers, 19

testing and evaluation, 22 validation, 22 Grammar of English Nominalizations, The (Lees), 18 Grammatical, 12 Grammatical sentence, 12 Grammatical terminology, traditional, 11 Grammaticality, 12, 13 Harris, Zellig S„ 17, 19 Hill, Archibald A., 12, 20 Hockett, Charles F., 12, 13 Hollander, John, fn 15 Idioms, 16 Intonational pattern symbol, 18 Introduction to Transformational Grammars, An (Bach), 10 Irard, Stephen, fn 26 Iterations, computer output 1, 97 2, 105 3, 111 4, 117 5, 124 6, 130 7, 136 8, 145 9, 151 10, 157 11, 162 12, 170 13, 175 14, 181 15, 188 16, 195 17, 202 18, 208 19, 215 20, 222

439

INDEX

21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,

229 236 242 247 255 261 266 274 281 287 295 301 309 316 323 329 335 341 348 355 363 370 377 384 390 396 401 409 415 423

Jakobson, Roman, 13 Kernel-generating routine, 24 Lamb, Sydney, 23 Larbaud, Valéry, fn 15 Lees, Robert B., 18, 20, 21 Limitations of this grammar, 17 Locke, W. N., fn 22 Maclay, Howard, fn 13 Marks, Lawrance E., fn 26 METEOR, 24 Mehler, Jacques, 26 Miller, George A., 26 Moyne, John, fn 15 Nida, Eugene A., 16 Notational system, 10 Noun phrase routine, 24

One-string transformation routine, 24 Pangasinan, 17 Parallelism, syntactic-semantic, 9 Parsing, automatic, 25 Pedagogical materials, 25 Phonological grammar, validation of, 23 Psycholinguistics, 26, 27 Quine, Williard V., fn 15 Random generation, 7, 14, 22, 25 Recursiveness, 22 Reference grammar, 25 Reichens, R. H., fn 14 Rule numbers of principal symbols, 431 Saporta, Sol, 23 Satterthwait, Arnold Chase, 20 Schachter, Paul, 17, 18, 19 Semantic classifications, 24 Semantic equivalent, 14 Semantic rules, 14 Sentence, grammatical, 12 Sentence, pseudo, 12 Skewing, 18 Sleator, Mary D., fn 13 SNOBOL, 24 Stockwell, Robert P., 12, 13, 18 Subscripts, explanation of, 432—33 Superscripts, explanation of, 432—33 Symbols, principal explanation of, 431—32 rule numbers of, 431 Symbols, special in computer output, 433 in this grammar, 431 Syntactic-semantic parallelism, 9 Thai, 19 Transfer grammar, 17 Translation, 14 Two-string transformation routine, 25 Validation of grammars, 22 Verb phrase routine, 24 Yngve, Victor H., 14, 20, 22