國立虎尾科技大學 |

Deep learning in natural language processing

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning in natural language processing/ edited by Li Deng, Yang Liu.
other author:	Deng, Li.
Published:	Singapore :Springer Singapore : : 2018.,
Description:	xvii, 329 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Natural language processing (Computer science) -
Online resource:	http://dx.doi.org/10.1007/978-981-10-5209-5
ISBN:	9789811052095

Deep learning in natural language processing
Deep learning in natural language processing[electronic resource] /edited by Li Deng, Yang Liu. - Singapore :Springer Singapore :2018. - xvii, 329 p. :ill., digital ;24 cm.

Chapter 1: A Joint Introduction to Natural Language Processing and to Deep Learning -- Chapter 2: Deep Learning in Conversational Language Understanding -- Chapter 3: Deep Learning in Spoken and Text-Based Dialogue Systems -- Chapter 4: Deep Learning in Lexical Analysis and Parsing -- Chapter 5: Deep Learning in Knowledge Graph -- Chapter 6: Deep Learning in Machine Translation -- Chapter 7: Deep Learning in Question Answering -- Chapter 8: Deep Learning in Sentiment Analysis -- Chapter 9: Deep Learning in Social Computing -- Chapter 10: Deep Learning in Natural Language Generation from Images -- 11. Epilogue: Frontiers of NLP in the Deep Learning Era.

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.

ISBN: 9789811052095

Standard No.: 10.1007/978-981-10-5209-5doiSubjects--Topical Terms:

641811
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Deep learning in natural language processing
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505 0 $a Chapter 1: A Joint Introduction to Natural Language Processing and to Deep Learning -- Chapter 2: Deep Learning in Conversational Language Understanding -- Chapter 3: Deep Learning in Spoken and Text-Based Dialogue Systems -- Chapter 4: Deep Learning in Lexical Analysis and Parsing -- Chapter 5: Deep Learning in Knowledge Graph -- Chapter 6: Deep Learning in Machine Translation -- Chapter 7: Deep Learning in Question Answering -- Chapter 8: Deep Learning in Sentiment Analysis -- Chapter 9: Deep Learning in Social Computing -- Chapter 10: Deep Learning in Natural Language Generation from Images -- 11. Epilogue: Frontiers of NLP in the Deep Learning Era.
520 $a 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.
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