Neural Network Methods for Natural Language Processing

Description

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning. 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