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Table of contents (21 chapters)
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Front Matter
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Supervised Classification and Feed-forward Neural Networks
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Front Matter
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About this book
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Neural Network Methods for Natural Language Processing
Authors: Yoav Goldberg
Series Title: Synthesis Lectures on Human Language Technologies
DOI: https://doi.org/10.1007/978-3-031-02165-7
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 7
Copyright Information: Springer Nature Switzerland AG 2017
Softcover ISBN: 978-3-031-01037-8Published: 17 April 2017
eBook ISBN: 978-3-031-02165-7Published: 01 June 2022
Series ISSN: 1947-4040
Series E-ISSN: 1947-4059
Edition Number: 1
Number of Pages: CCXCII, 20
Topics: Artificial Intelligence, Natural Language Processing (NLP), Computational Linguistics