Neural Network Methods for Natural Language Processing
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…
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Produktdetails
- ISBN: 978-3-031-01037-8
- EAN: 9783031010378
- Produktnummer: 39048804
- Verlag: Springer International Publishing
- Sprache: Englisch
- Erscheinungsjahr: 2017
- Seitenangabe: 312 S.
- Masse: H23.5 cm x B19.1 cm x D1.6 cm 586 g
- Abbildungen: Paperback
- Gewicht: 586
Über den Autor
Yoav Goldberg has been working in natural language processing for over a decade. He is a Senior Lecturer at the Computer Science Department at Bar-Ilan University, Israel. Prior to that, he was a researcher at Google Research, New York. He received his Ph.D. in Computer Science and Natural Language Processing from Ben Gurion University (2011). He regularly reviews for NLP and machine learning venues, and serves at the editorial board of Computational Linguistics. He published over 50 research papers and received best paper and outstanding paper awards at major natural language processing conferences. His research interests include machine learning for natural language, structured prediction, syntactic parsing, processing of morphologically rich languages, and, in the past two years, neural network models with a focus on recurrent neural networks.
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