Neural Network Methods for Natural Language Processing

Yoav Goldberg

• Publisher: Springer
• Year: 2022
• Print ISBN: 9783031010378, 9783031001765
• eBook ISBN: 9783031021657
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

Table of contents

• Cover Page
• Copyright Page
• Title Page
• Contents
• Preface
• Acknowledgments
• Introduction
• Supervised Classification and Feed-forward Neural Networks
• Working with Natural Language Data
• Specialized Architectures
• Additional Topics
• Bibliography
• Author’s Biography