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.

Autorentext

Yoav Goldberg has been working in natural language processing for over a decade. He is a Senior Lecturer at the Computer Science Department at Bar-Ilan University, Israel. Prior to that, he was a researcher at Google Research, New York. He received his Ph.D. in Computer Science and Natural Language Processing from Ben Gurion University (2011). He regularly reviews for NLP and machine learning venues, and serves at the editorial board of Computational Linguistics. He published over 50 research papers and received best paper and outstanding paper awards at major natural language processing conferences. His research interests include machine learning for natural language, structured prediction, syntactic parsing, processing of morphologically rich languages, and, in the past two years, neural network models with a focus on recurrent neural networks.

Inhalt
Preface.- Acknowledgments.- Introduction.- Learning Basics and Linear Models.- Learning Basics and Linear Models.- From Linear Models to Multi-layer Perceptrons.- Feed-forward Neural Networks.- Neural Network Training.- Features for Textual Data.- Case Studies of NLP Features.- From Textual Features to Inputs.- Language Modeling.- Pre-trained Word Representations.- Pre-trained Word Representations.- Using Word Embeddings.- Case Study: A Feed-forward Architecture for Sentence.- Case Study: A Feed-forward Architecture for Sentence Meaning Inference.- Ngram Detectors: Convolutional Neural Networks.- Recurrent Neural Networks: Modeling Sequences and Stacks.- Concrete Recurrent Neural Network Architectures.- Modeling with Recurrent Networks.- Modeling with Recurrent Networks.- Conditioned Generation.- Modeling Trees with Recursive Neural Networks.- Modeling Trees with Recursive Neural Networks.- Structured Output Prediction.- Cascaded, Multi-task and Semi-supervised Learning.- Conclusion.-Bibliography.- Author's Biography.