Information Retrieval Technology: 6th Asia Information Retrieval Societies Conference, AIRS 2010, Taipei, Taiwan, December 1-3, 2010, Proceedings 3642171869, 9783642171864, 9783642171871, 3642171877

The Asia Information Retrieval Societies Conference (AIRS) 2010 was the sixth conference in the AIRS series, aiming to b

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Information Retrieval Technology: 6th Asia Information Retrieval Societies Conference, AIRS 2010, Taipei, Taiwan, December 1-3, 2010, Proceedings
 3642171869, 9783642171864, 9783642171871, 3642171877

Table of contents :
Cover......Page 1
Lecture Notes in Computer Science 6458......Page 2
Information RetrievalTechnology......Page 3
ISBN-13 9783642171864......Page 4
Preface......Page 6
Organization......Page 8
Table of Contents......Page 14
Introduction......Page 20
Related Work......Page 21
General Model......Page 22
BM25 Kernel......Page 23
LMIR Kernel......Page 24
KL Kernel......Page 25
Relation to Conventional Models......Page 26
Efficient Implementation......Page 27
Experiments on the Web Search Dataset......Page 28
Experiments on the OHSUMED and the AP Datasets......Page 29
Conclusion......Page 30
References......Page 31
Introduction......Page 32
Seed Dataset (Entry-Point)......Page 35
Video and User Properties......Page 36
Linguistic Analysis of User Comments......Page 37
Social Network Analysis......Page 38
Discussion......Page 42
References......Page 43
Introduction......Page 44
Related Work......Page 45
Methodology......Page 46
Term Type Prediction......Page 47
Retrieval Model......Page 48
Data Collection......Page 49
Term Type Prediction Evaluation......Page 50
Retrieval Model Evaluation......Page 52
An Example......Page 53
References......Page 54
Introduction......Page 56
The Generative Framework......Page 58
Inference......Page 59
Computational Efficiency......Page 60
Query Refinement Using the Secondary Representation......Page 61
Retrieving Query-Relevant Facets......Page 63
Related Work......Page 65
References......Page 66
Introduction......Page 68
Background and Related Work......Page 69
Our Novel Similarity Measure......Page 70
The Clustering Criterion Functions......Page 71
Optimization Algorithm......Page 73
Experimental Setup and Evaluation Metrics......Page 74
Results......Page 75
Conclusions and Future Work......Page 78
References......Page 79
Introduction......Page 80
Train the Initial Naïve Bayes Classifier......Page 81
Ratio-Adjusted EM Steps......Page 83
Dataset......Page 84
Overall Performance......Page 85
Study on the Effectiveness of R-Step and Sensitivity of......Page 86
Study on Sensitivity of $\hat\gamma$......Page 87
Related Work......Page 88
References......Page 90
Introduction......Page 92
Related Work......Page 93
Precision as Effective Time Ratio......Page 94
Effective Time Ratio for Search Engines with Snippets......Page 95
Theoretical Analysis......Page 96
Experiment......Page 97
Data Collecting......Page 98
Metrics......Page 99
Basic Results......Page 100
Conclusion......Page 101
References......Page 102
Introduction......Page 104
Related Work......Page 105
Data......Page 106
Query Level Analysis......Page 107
Session Level Analysis......Page 108
Query and Non-click Behavior......Page 109
Non-click and Post-query Actions in Session......Page 111
Non-click and Users’ Click Preference......Page 113
Conclusions and Future Work......Page 114
References......Page 115
Introduction......Page 116
Retrieval Experimentation......Page 117
Score Estimation......Page 118
Experimental Investigation......Page 121
Conclusion......Page 126
References......Page 127
Introduction......Page 129
Meta-search Engine Construction......Page 130
Multi-search Architecture......Page 131
Details of the Proposed Methods in Multi-Search......Page 132
Query Translation and Returned Search Results Merging......Page 133
Experimental Results......Page 135
Evaluation of the Study......Page 136
References......Page 138
Introduction......Page 140
Overview......Page 141
Two-Layer Graph Model......Page 142
Ranking Homogeneous Objects......Page 143
Co-ranking Heterogeneous Objects......Page 145
Dataset and Evaluation Metrics......Page 146
Experimental Results......Page 147
References......Page 148
Introduction......Page 150
Related Work......Page 151
Generation of Transformation Rules......Page 152
Automatically Accepting Evidences......Page 153
Temporal Evaluation......Page 155
Baseline......Page 157
Conclusion and Future Work......Page 158
References......Page 159
Introduction......Page 160
Related Research......Page 161
The Approach......Page 162
The Proposed Architecture......Page 163
Document Annotation......Page 165
Semantic Search and Processing......Page 167
Conclusion......Page 168
References......Page 169
Introduction......Page 170
Rocchio's Relevance Feedback Method......Page 171
The DFR Probabilistic Framework......Page 172
Quality-Biased PRF......Page 173
Test Collections and Evaluation......Page 174
Performance of Basic Retrieval Models......Page 175
Comparison of the PRF Methods......Page 176
Conclusions and Future Work......Page 178
References......Page 179
Introduction......Page 181
Subtopic Aware Paradigm for Diversity......Page 183
Integration Approach......Page 184
Empirical Study......Page 186
Experimental Results......Page 188
References......Page 190
Introduction......Page 192
Related Work......Page 193
Experiment......Page 194
Methods of Analysis......Page 195
Overview of Results on Search Units......Page 196
Results of Actions in Each Search Unit......Page 197
Results of Eye Gaze Points for SERP......Page 198
Results of View Rank and Click Rank......Page 199
Discussion and Conclusion......Page 200
References......Page 201
Introduction......Page 202
Background and Related Work......Page 203
Retrieval Model......Page 204
Experimental Setup......Page 206
Results and Discussion......Page 207
References......Page 210
Introduction......Page 212
Background and Related Research......Page 213
The Approach......Page 214
Extraction of Image Description, URL and Surrounding Text......Page 215
Syntactic Analysis......Page 216
Evaluation......Page 218
Discussion and Conclusion......Page 220
References......Page 221
Introduction......Page 222
Cost-Sensitive Listwise Approach......Page 223
Conditions of Order Preservation for Cost-Sensitive Listwise Approach......Page 224
Generalization for Order Preserved Cost-Sensitive Listwise Approach......Page 225
A Case: Order Preserved Cost-Sensitive ListMLE Approach......Page 226
Experiment Results......Page 227
Conclusion......Page 228
References......Page 229
Introduction......Page 230
Related Work......Page 231
Motivation......Page 232
Query Expansion Methods......Page 233
Maximum Relevance and Minimum Redundancy Criterion for Query Expansion (mRMR-QE)......Page 234
Collections......Page 235
mRMR-QE Evaluation......Page 236
Conclusions......Page 237
References......Page 238
Introduction......Page 240
Frameworks of Conditional Markov Models......Page 241
Hybrid Deterministic and Nondeterministic Inference Algorithm......Page 242
Automatic Chunk Relation Construction......Page 243
Speed-Up Local Classifiers......Page 244
Experiments......Page 245
Overall Results......Page 246
References......Page 248
Introduction......Page 250
Related Work......Page 251
Problem Formulation......Page 252
FolkRank......Page 253
FolkDiffusion......Page 254
Comparing with Other Methods......Page 256
Conclusion and Future Work......Page 258
References......Page 259
Introduction......Page 260
Feature Extraction......Page 261
Problem Formulation......Page 262
Entropy-and-Relevance-Based Summarization......Page 263
Regression-Based Summarization......Page 264
Experimental Setting......Page 265
Performance Evaluation......Page 266
Related Work......Page 267
Conclusion......Page 268
References......Page 269
Introduction......Page 270
Decision Tree Induction......Page 271
Support Vector Machines......Page 272
Spammer Behavioral Patterns......Page 273
Email Corpus......Page 275
Evaluation Metrics......Page 276
Experimental Results and Discussion......Page 277
Conclusion and Future Work......Page 278
References......Page 279
Related Work......Page 280
Our Approach......Page 281
Tagging with CRF model......Page 283
Experimental Strategy and Results......Page 284
Conclusions......Page 287
References......Page 288
Introduction......Page 289
Related Work......Page 290
Preliminaries......Page 291
Evaluation of the Semantic Relation Classification Performance......Page 293
Robustness with Respect to the Inter-view Correlation Measure......Page 296
References......Page 297
Introduction......Page 299
Related Research......Page 300
N-Gram Conflation and Co-occurrence Analysis for Language-Independent and Corpus-Based Stemming......Page 302
Experiments......Page 304
Conclusion and Future Work......Page 306
References......Page 307
Introduction......Page 309
The Stemming Inversion Problem......Page 310
Procedure......Page 313
Results......Page 314
References......Page 317
Introduction......Page 319
Pattern Matching......Page 320
Supervised IE......Page 321
More Queries and Fewer Answers......Page 322
Statistical Re-ranking......Page 323
Data and Scoring Metric......Page 324
System/Human Comparison......Page 325
Impact of Statistical Re-ranking......Page 326
Conclusion......Page 327
References......Page 328
Introduction......Page 329
Related Work......Page 330
Method......Page 331
Relation Extraction between Concepts by Web Search......Page 332
Experimental Environment......Page 334
Experimental Results......Page 335
Extracted Relations......Page 336
Conclusion......Page 337
References......Page 338
Introduction......Page 339
Problem Definition......Page 341
Score Function for Opinion-Oriented Chinese Sentence Compression......Page 343
Experiment Setup......Page 344
Experiment Results......Page 345
Conclusion and Future Work......Page 346
References......Page 347
Introduction......Page 349
Related Work......Page 350
Definition of Conditional Random Field......Page 351
Relations in Vietnamese......Page 352
Relation Extraction......Page 354
Experiments and Discussion......Page 355
Conclusions......Page 357
References......Page 358
Introduction......Page 359
Sparse L$_2$-Regularized SVMs Optimization......Page 360
Speed-Up Local Classifiers......Page 362
Search the Optimal Feature Set......Page 363
Results......Page 364
References......Page 367
Introduction......Page 369
Related Work......Page 370
The Compared Chinese Question Classifiers......Page 371
Test-Assisted Rule-Based Question Classifier......Page 372
The SVM-Based Question Classifier......Page 373
The Datasets......Page 374
Discussion......Page 375
References......Page 377
Introduction......Page 379
Related Work......Page 380
Introduction of the Model and Q-Function......Page 381
The EM Algorithm......Page 382
Experimental Setting......Page 384
Results......Page 385
References......Page 387
Introduction......Page 389
Related Work......Page 390
The KG-DRank Algorithm......Page 391
Baseline......Page 393
Comparison to Baseline......Page 394
Conclusion......Page 396
References......Page 397
Introduction......Page 398
An Interactive CLIR Interface......Page 399
Experiment to Collect Human Assessments......Page 400
Semantic Class and Relevance......Page 402
Familiarity and Relevance......Page 404
Conclusive Remarks......Page 406
References......Page 407
Introduction......Page 408
Methodology......Page 410
Evaluation Protocols......Page 411
Our Approach for Personalizing Mobile Search Using a Spatio-Temporal User Profile......Page 412
Evaluation Framework Application......Page 414
Measuring Results Consistency over the Two Evaluation Protocols......Page 415
Conclusion......Page 416
References......Page 417
Introduction......Page 418
Data Set......Page 419
Rule-Based Classifier......Page 420
Machine Learning Classifier......Page 421
Research Questions and Methodology......Page 423
Combination of Query Intents......Page 424
Patterns of Query Intents......Page 425
Query Intents and Query Re-formulation......Page 426
References......Page 427
Introduction......Page 429
Related Work......Page 430
Query Recommendation with Good User Experience......Page 431
User Behavior Features......Page 432
Training Data Annotation......Page 434
Evaluation of Recommendation’s Relevance and Search Performance......Page 435
Popularity of Recommended Queries......Page 436
Conclusion and Future Work......Page 437
References......Page 438
Introduction......Page 439
The Model......Page 441
Unsupervised Learning and the Rules......Page 442
Interpolated Kneser-Ney Smoothing......Page 443
Adapted Gibbs Sampling......Page 444
Experiment Setup......Page 446
Supervised Learning......Page 447
Unsupervised Learning......Page 448
Discussion and Conclusion......Page 449
References......Page 450
Introduction......Page 451
Cross-Document IE Annotation......Page 452
Motivation of Using IE for Summarization......Page 453
Relations/Events Can Push Up Relevant Sentences......Page 454
Event Coreference Can Remove Redundancy......Page 455
IE-Based Re-ranking and Redundancy Removal......Page 456
TAC Responsiveness Scores......Page 458
Discussion......Page 459
Conclusion......Page 460
References......Page 461
Introduction......Page 462
The Transition Stage......Page 463
The Transmission Stage......Page 465
Baseline Systems......Page 467
Our Approach......Page 468
Classification......Page 469
Conclusions......Page 470
References......Page 471
Introduction......Page 473
Related Work......Page 474
Domain-Topic Model......Page 475
Distributed Gibbs Sampling of DTM......Page 476
Ranking Terms Using DTM......Page 477
Keyword Extraction......Page 478
Content-Based Tag Recommendation......Page 481
References......Page 483
Introduction......Page 485
Graph Models for Polarity Lexicon Induction......Page 486
Analysis of the Two Kinds of Models......Page 487
Graph Models for Polarity Lexicon Induction......Page 488
Polarity Lexicon Induction with Morphological Features......Page 489
Integrating Graph Models and Morphological Features......Page 490
Experiments with Graph Models......Page 491
Experiments with Models of Morphological Features......Page 492
Experiments on Integration......Page 493
Discussion......Page 494
Conclusion and Future Work......Page 495
References......Page 496
Introduction......Page 497
Supplementary Data Assisted Ranking......Page 499
Supplementary Learning to Rank......Page 500
Boosting-Based Algorithms......Page 501
RankBoost-Heter......Page 502
Supplementary Ranking on Homogeneous Data......Page 504
Discussions......Page 506
References......Page 508
Introduction......Page 509
Temporal TextTiling Model......Page 511
Temporal TextTiling......Page 512
Context Similarity......Page 513
Named Entity Influence......Page 514
Temporal Proximity......Page 515
Evaluation Metrics......Page 516
Performance and Discussion......Page 517
Conclusion......Page 519
References......Page 520
Introduction......Page 521
Machine Learning and Sequence Labeling Tasks......Page 523
Impact of Varying the Model Size......Page 525
Experimental Results......Page 526
Improving Data Caching......Page 529
References......Page 532
Introduction......Page 533
Risk Minimization Framework......Page 535
Computing the Optimal Scores......Page 536
Considering True Relevance Feedback......Page 539
Experimental Setup......Page 540
The Effectiveness of the Iterative Optimization Algorithm......Page 541
Effect of Considering Relevance Feedback Information......Page 542
Related Work......Page 543
Conclusions......Page 544
References......Page 545
Introduction......Page 546
Smoothness of QL as the Document Weight......Page 548
Improving Lower Weights......Page 550
Evaluation Configuration......Page 552
Evaluation on Weight Allocation Methods......Page 554
Discussion......Page 555
Conclusions and Future Work......Page 556
References......Page 557
Introduction......Page 558
Related Work......Page 560
Variable Dependency Model......Page 561
Parameter Estimation......Page 563
Test Collections......Page 564
Experimental Results......Page 565
Analysis and Discussion......Page 568
References......Page 569
Introduction......Page 571
Proposed Method......Page 573
Clues Extraction......Page 574
Query Generation and Ranking......Page 575
Filtering......Page 576
Experiment Setup......Page 577
Experiment Results and Discussion......Page 578
Discussion......Page 580
References......Page 581
Introduction......Page 583
Query Expansion......Page 584
Query-Click Graph......Page 585
Term-Relationship Graph......Page 586
Naïve Method......Page 587
Pruning......Page 588
Query Expansion......Page 589
Design of Experiments......Page 590
Results......Page 591
References......Page 593
Introduction......Page 595
Related Work......Page 596
Bilingual Snippets Collection......Page 597
Candidates Extraction......Page 598
Frequency Distance Model......Page 599
Transliteration Model......Page 600
Snippets Collection Experiment......Page 601
OOV Translation Selection Experiments......Page 602
CLIR Experiments......Page 603
References......Page 605
Introduction......Page 607
Modification of the Score Based on the Boolean Query......Page 608
Differences between WWW Document Retrieval and QA Retrieval......Page 610
Query Construction Using Synonyms and Variation Lists......Page 611
Discussion of the Experimental Results......Page 614
Conclusion......Page 616
References......Page 617
Introduction......Page 618
Related Work......Page 619
Co_Tags Semantic Similarity......Page 620
T_SimRank Semantic Similarity......Page 621
T_PageRank Popularity......Page 622
Datasets and Evaluation Measure......Page 624
Experiment Result......Page 625
Conclusions......Page 627
References......Page 628
Introduction......Page 629
Image Re-ranking Based on Quality......Page 631
Image Features......Page 632
Datasets......Page 636
Ranking Performance......Page 637
Feature Analysis......Page 639
References......Page 641
Author Index......Page 644

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