Neural Network Methods for Natural Language Processing

Yoav Goldberg

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English

Springer International Publishing AG
17 April 2017

Computational linguistics; Artificial intelligence; Natural language & machine translation

Series: Synthesis Lectures on Human Language Technologies

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.

By: Yoav Goldberg
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Dimensions: Height: 235mm, Width: 191mm,
Weight: 591g
ISBN: 9783031010378
ISBN 10: 303101037X
Series: Synthesis Lectures on Human Language Technologies
Pages: 20
Publication Date: 17 April 2017
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.