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Neural Network Methods in Natural Language Processing

By Yoav Goldberg

Neural Network Methods in Natural Language Processing Computer Science

Neural Network Methods in Natural Language Processing

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Description
Neural networks are a family of powerful machine learning models and this book focuses on their application 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.
Product details
Binding:
Paperback
Number of Pages:
309
Release Date:
2017-04-17
Publication Date:
2017-04-17
Publisher:
MORGAN & CLAYPOOL
Languages:
Original: English
ISBN10:
1627052984
ISBN13:
9781627052986
Weight:
582 g
Height:
191 cm
Width:
235 cm
Thickness:
16 cm
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