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Neural network methods for natural l...

Goldberg, Yoav,

Neural network methods for natural language processing /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neural network methods for natural language processing // Yoav Goldberg.
作者:	Goldberg, Yoav,
出版者:	[San Rafael, California] :Morgan & Claypool, : c2017. ,
面頁冊數:	xxii, 287 p. :ill. ; : 24 cm. ;
附註:	Part of: Synthesis digital library of engineering and computer science
標題:	Natural language processing (Computer science) -
ISBN:	9781627052986 (pbk.) :

Neural network methods for natural language processing /
Goldberg, Yoav,

Neural network methods for natural language processing /Yoav Goldberg. - [San Rafael, California] :Morgan & Claypool,c2017. - xxii, 287 p. :ill. ;24 cm. - Synthesis lectures on human language technologies,# 37 1947-4040 ;. - Synthesis lectures on human language technologies ;#18. .

Part of: Synthesis digital library of engineering and computer science

Includes bibliographical references (p. 253-285)

1. Introduction -- 2. Learning basics and linear models --3. From linear models to multi-layer perceptrons -- 4. Feed-forward neural networks -- 5. Neural network training -- 6. Features for textual data -- 7. Case studies of NLP features -- 8. From textual features to inputs -- 9. Language modeling -- 10. Pre-trained word representations -- 11. Using word embeddings -- 12. Case study: a feed-forward architecture for sentence meaning inference -- 13. Ngram detectors: convolutional neural networks -- 14. Recurrent neural networks: modeling sequences and stacks -- 15. Concrete recurrent neural network architectures -- 16. Modeling with recurrent networks -- 17. Conditioned generation -- 18. Modeling trees with recursive neural networks -- 19. Structured output prediction -- 20. Cascaded, multi-task and semi-supervised learning -- 21. Conclusion -- Bibliography -- Author's biography

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 ofthe 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 trainarbitrary 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

ISBN: 9781627052986 (pbk.) :NT2198Subjects--Topical Terms:

931367
Natural language processing (Computer science)

Dewey Class. No.: 006.35

Neural network methods for natural language processing /
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020 $z 9781627052955 (ebook)
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100 1 $a Goldberg, Yoav, $e author $3 1137470
245 1 0 $a Neural network methods for natural language processing / $c Yoav Goldberg.
260 # $a [San Rafael, California] : $b Morgan & Claypool, $c c2017.
300 $a xxii, 287 p. : $b ill. ; $c 24 cm.
490 1 $a Synthesis lectures on human language technologies, $x 1947-4040 ; $v # 37
500 $a Part of: Synthesis digital library of engineering and computer science
504 $a Includes bibliographical references (p. 253-285)
505 0 # $a 1. Introduction -- 2. Learning basics and linear models --3. From linear models to multi-layer perceptrons -- 4. Feed-forward neural networks -- 5. Neural network training -- 6. Features for textual data -- 7. Case studies of NLP features -- 8. From textual features to inputs -- 9. Language modeling -- 10. Pre-trained word representations -- 11. Using word embeddings -- 12. Case study: a feed-forward architecture for sentence meaning inference -- 13. Ngram detectors: convolutional neural networks -- 14. Recurrent neural networks: modeling sequences and stacks -- 15. Concrete recurrent neural network architectures -- 16. Modeling with recurrent networks -- 17. Conditioned generation -- 18. Modeling trees with recursive neural networks -- 19. Structured output prediction -- 20. Cascaded, multi-task and semi-supervised learning -- 21. Conclusion -- Bibliography -- Author's biography
520 3 $a 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 ofthe 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 trainarbitrary 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
650 # 0 $a Natural language processing (Computer science) $3 931367
650 # 0 $a Neural networks (Computer science) $3 676148
830 0 $a Synthesis lectures on human language technologies ; $v #18. $3 931364