Neural Network Methods for Natural Language Processing 9781627052955
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network
1,888 200 3MB
English Pages 287 Year 2017
Table of contents :
Preface
Acknowledgments
1 Introduction
PART I Supervised Classification and Feed-forward Neural Networks
2 Learning Basics and Linear Models
3 From Linear Models to Multi-layer Perceptrons
4 Feed-forward Neural Networks
5 Neural Network Training
PART II Working with Natural Language Data
6 Features for Textual Data
7 Case Studies of NLP Features
8 From Textual Features to Inputs
9 Language Modeling
10 Pre-trained Word Representations
11 Using Word Embeddings
12 Case Study: A Feed-forward Architecture for Sentence Meaning Inference
PART III Specialized Architectures
13 Ngram Detectors: Convolutional Neural Networks
14 Recurrent Neural Networks: Modeling Sequences and Stacks
15 Concrete Recurrent Neural Network Architectures
16 Modeling with Recurrent Networks
17 Conditioned Generation
PART IV Additional Topics
18 Modeling Trees with Recursive Neural Networks
19 Structured Output Prediction
20 Cascaded, Multi-task and Semi-supervised Learning
21 Conclusion
Bibliography
Author’s Biography
![Natural Language Processing With Java: Techniques for Building Machine Learning and Neural Network Models for NLP [2 ed.]
1788993497](https://dokumen.pub/img/200x200/natural-language-processing-with-java-techniques-for-building-machine-learning-and-neural-network-models-for-nlp-2nbsped-1788993497.jpg)
