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.
Author(s): Yoav Goldberg
Series: Synthesis Lectures on Human Language Technologies
Publisher: Morgan & Claypool
Year: 2017
Language: English
Pages: 287
Tags: Natural Language Processing, Neural Networks
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
