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Series ISSN 1947-4040

store.morganclaypool.com

Series Editor: Graeme Hirst, University of Toronto

Neural Network Methods for Natural Language Processing

Yoav Goldberg, Bar Ilan University

Neural networks are a family of powerful machine learning models. is book focuses on the

application of neural network models to natural language data. e rst 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 dene and train arbitrary neural networks, and is the basis behind the design of

contemporary neural network software libraries.

e 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. ese 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.

ABOUT SYNTHESIS

is volume is a printed version of a work that appears in the Synthesis

Digital Library of Engineering and Computer Science. Synthesis lectures

provide concise original presentations of important research and

development topics, published quickly in digital and print formats. For

more information, visit our website: http://store.morganclaypool.com

GOLDBERG

NEURAL NETWORK METHODS

FOR NATURAL LANGUAGE PROCESSING

MORGAN & CLAYPOOL

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