Deep Learning in Natural Language Processing (PDF)
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In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence.
This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided.
The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
Yang Liu is an associate professor at the Department of Computer Science and Technology, Tsinghua University. He received his PhD degree from the Chinese Academy of Sciences Institute of Computing Technology in 2007. His research focuses on natural language processing and machine translation. He has published over 50 papers in leading NLP/AI journals and conferences such as Computational Linguistics, ACL, AAAI, EMNLP, and COLING. He won the COLING/ACL 2006 Meritorious Asian NLP Paper Award and the National Science and Technology Progress Award
- 2018, 1st ed. 2018, 329 Seiten, Englisch
- Herausgegeben: Li Deng, Yang Liu
- Verlag: Springer-Verlag GmbH
- ISBN-10: 9811052093
- ISBN-13: 9789811052095
- Erscheinungsdatum: 23.05.2018
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- Größe: 10 MB
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