Neural Network Methods in Natural Language Processing

Nonfiction, Computers, Advanced Computing, Natural Language Processing, Artificial Intelligence, Reference & Language, Language Arts, Linguistics
Cover of the book Neural Network Methods in Natural Language Processing by Yoav Goldberg, Graeme Hirst, Morgan & Claypool Publishers
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Author: Yoav Goldberg, Graeme Hirst ISBN: 9781681731551
Publisher: Morgan & Claypool Publishers Publication: April 17, 2017
Imprint: Morgan & Claypool Publishers Language: English
Author: Yoav Goldberg, Graeme Hirst
ISBN: 9781681731551
Publisher: Morgan & Claypool Publishers
Publication: April 17, 2017
Imprint: Morgan & Claypool Publishers
Language: English

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.

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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.

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