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推荐理由:本书适合有一定基础的读者学习。本书由语音识别泰斗邓力老师和自然语言处理大牛刘洋老师合著,系统介绍深度学习在NLP常见问题中的应用,是目前对于此方面研究最新、最全面的综述。基于这些分析,对NLP未来发展的研究方向进行了探讨,包括神经符号整合框架、基于记忆的模型、先验知识融合以及深度学习范式等。2018最新力作,关注深度学习和自然领域处理的小伙伴不容错过。
Li Deng. Yang liu Editors Deep learning in natural Language Processing 空 Springer editors Li deng Yang Liu AI Research at citadel Tsinghua University Chicago, IL B g USA China AI Research at citadel Seattle. wa USA ISBN978-981-10-5208-8 ISBN978-981-10-5209-5( eBook) https:/doi.org/10.1007/978-981-10-5209-5 Library of Congress Control Number: 2018934459 C Springer Nature Singapore Pte Ltd. 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Printed on acid-free paper This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd part of Springer Nature The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721 Singapore Foreword Written by a group of the most active researchers in the field, led by Dr Deng,an nternationally respected expert in both NLP and deep learning this book provides a comprehensive introduction to and up-to-date review of the state of art in applying deep learning to solve fundamental problems in NLP. Further, the book is highly timely, as demands for high-quality and up-to-date textbooks and research refer ences have risen dramatically in response to the tremendous strides in deep learning applications to NLP. The book offers a unique reference guide for practitioners in are becoming an essential enabler and a core differentiator. re nlp technologies various sectors, especially the Internet and AI start-ups, whe Hongjiang Zhang(Founder, Sourcecode Capital; former CEO of King Soft) This book provides a comprehensive introduction to the latest advances in deep learning applied to NLP. Written by experienced and aspiring deep learning and NLP researchers, it covers a broad range of major NLP applications, includin spoken language understanding, dialog systems, lexical analysis, parsing, knowl edge graph, machine translation, question answering, sentiment analysis, and social computing. The book is clearly structured and moves from major research trends, to the atest deep learning approaches, to their limitations and promising future work Given its self-contained content sophisticated algorithms , and detailed use cases the book offers a valuable guide for all readers who are working on or learning about deep learning and NLP. Haifeng Wang (Vice President and Head of research, Baidu; former President of ACL) In 2011, at the dawn of deep learning in industry, I estimated that in most speech recognition applications, computers still made 5 to 10 times more errors than human subjects, and highlighted the importance of knowledge engineering in future directions. Within only a handful of years since, deep learning has nearly closed the gap in the accuracy of conversational speech recognition between human and computers. Edited and written by Dr. Li Denga pioneer in the recent speech Foreword recognition revolution using deep learningand his colleagues, this book elegantly describes this part of the fascinating history of speech recognition as an important subfield of natural language processing (NLP). Further, the book expands this historical perspective from speech recognition to more general areas of NLP, offering a truly valuable guide for the future development of NLP. Importantly, the book puts forward a thesis that the current deep learning trend is a revolution from the previous data-driven(shallow )machine learning era, although ostensibly deep learning appears to be merely exploiting more data, more com- puting power, and more complex models. Indeed, as the book correctly points out, the current state of the art of deep learning technology developed for NLP appli cations, despite being highly successful in solving individual NLP tasks, has not taken full advantage of rich world knowledge or human cognitive capabilities Therefore, I fully embrace the view expressed by the books editors and authors that more advanced deep learning that seamlessly integrates know ledge engineering will pave the way for the next revolution in NLP I highly recommend speech and NLP researchers, engineers, and students to read this outstanding and timely book, not only to learn about the state of the art in NLP and deep learning, but also to gain vital insights into what the future of the NLP field will hold.” Sadaoki Furui(President, Toyota Technological Institute at Chicago Preface Natural language processing (NLP), which aims to enable computers to process human languages intelligently, is an important interdisciplinary field crossing artificial intelligence, computing science, cognitive science, information processing, and linguistics. Concerned with interactions between computers and human lar guages, NLP applications such as speech recognition, dialog systems, information retrieval, question answering, and machine translation have started to reshape the way people identify, obtain, and make use of information The development of NLP can be described in terms of three major waves rationalism, empiricism, and deep learning. In the first wave, rationalist approaches advocated the design of handcrafted rules to incorporate knowledge into NLP systems based on the assumption that know ledge of language in the human mind is fixed in advance by generic inheritance. In the second wave, empirical approaches assume that rich sensory input and the observable language data in surface form are required and sufficient to enable the mind to learn the detailed structure of natural language. As a result, probabilistic models were developed to discover the regu larities of languages from large corpora In the third wave, deep learning exploits hierarchical models of nonlinear processing, inspired by biological neural systems to learn intrinsic representations from language data, in ways that aim to simulate human cognitive abilities The intersection of deep learning and natural language processing has resulted in striking successes in practical tasks. Speech recognition is the first industrial NLP application that deep learning has strongly impacted. With the availability of arge-scale training data, deep neural networks achieved dramatically lower recognition errors than the traditional empirical approaches. Another prominent successful application of deep learning in NLP is machine translation. End-to-end neural machine translation that models the mapping between human languages using neural networks has proven to improve translation quality substantially Therefore neural machine translation has quickly become the new de facto tech- nology in major commercial online translation services offered by large technology companies: Google, Microsoft, Facebook, Baidu, and more Many other areas of NLP, including language understanding and dialog, lexical analysis and parsing, Preface knowledge graph, information retrieval, question answering from text, social computing, language generation, and text sentiment analysis, have also seen much significant progress using deep learning, riding on the third wave of NLP. Nowadays, deep learning is a dominating method applied to practically all nlP tasks The main goal of this book is to provide a comprehensive survey on the recent advances in deep learning applied to NLP. The book presents state of the art of NLP-centric deep learning research, and focuses on the role of deep learning played in major NLP applications including spoken language understanding, dialog sys tems, lexical analysis, parsing, knowledge graph, machine translation, question answering, sentiment analysis, social computing, and natural language generation (from images). This book is suitable for readers with a technical background in computation, including graduate students, post-doctoral researchers, educators, and industrial researchers and anyone interested in getting up to speed with the latest techniques of deep learning associated with NLP. The book is organized into eleven chapters as follows: Chapter 1: A Joint Introduction to Natural Language Processing and to Deep Learning (Li Deng and Yang Liu) Chapter 2: Deep learning in Conversational language Understanding( gokhan Tur, Asli Celikyilmaz, Xiaodong He, Dilek Hakkani-Tur, and Li Den Chapter 3: Deep Learning in Spoken and Text-Based Dialog Systems (Asli Celikyilmaz, Li Deng, and Dilek Hakkani-Tur) Chapter 4: Deep Learning in Lexical Analysis and Parsing(Wanxiang Che and Yue Zhang) Chapter 5: Deep Learning in Knowledge Graph(Zhiyuan Liu and Xianpei Han Chapter 6: Deep Learning in Machine Translation (Y ang Liu and Jiajun Zhang Chapter 7: Deep Learning in Question Answering(Kang Liu and Y ansong Feng Chapter 8: Deep Learning in Sentiment Analysis Duyu Tang and meishan Zhang) Chapter 9: Deep Learning in Social Computing (Xin Zhao and Chenliang Li) Chapter 10: Deep learning in Natural Language generation from Images (Xiaodong He and Li Deng Chapter 11: Epilogue(Li Deng and Y ang Liu) Chapter I first reviews the basics of nLp as well as the main scope of Nlp covered in the following chapters of the book, and then goes in some depth into the historical development of nlp summarized as three waves and future directions Subsequently, in Chaps. 2-10, an in-depth survey on the recent advances in deep learning applied to nLP is organized into nine separate chapters, each covering a largely independent application area of NLP. The main body of each chapter is written by leading researchers and experts actively working in the respective field The origin of this book was the set of comprehensive tutorials given at the 15th China National Conference on Computational Linguistics(CCL 2016) held in October 2016 in Yantai, Shandong, China, where both of us, editors of this book, Preface were active participants and were taking leading roles. We thank our Springer's senior editor, Dr. Celine Lanlan Chang, who kindly invited us to create this book and who has been providing much of timely assistance needed to complete this book. We are grateful also to Springers Assistant Editor, Jane li, for offering invaluable help through various stages of manuscript preparation We thank all authors of Chaps 2-10 who devoted their valuable time carefully preparing the content of their chapters: Gokhan Tur, Asli Celikyilmaz, Dilek Hakkani-Tur, Wanxiang Che, Yue Zhang, Xianpei Han, Zhiyuan Liu, Jiajun Zhang, Kang Liu, Yansong Feng, Duyu Tang, Meishan Zhang, Xin Zhao, Chenliang li and Xiaodong He. The authors of Chaps. 4-9 are CCL 2016 tutorial speakers. They spent a considerable amount of time in updating their tutorial material with the latest advances in the field since october 2016 Further, we thank numerous reviewers and readers, Sadaoki Furui, Andrew ng Fred Juang, Ken Church, Haifeng Wang, and Hongjiang Zhang, who not only gave us much needed encouragements but also offered many constructive comments which substantially improved earlier drafts of the book Finally, we give our appreciations to our organizations, Microsoft Research and Citadel (for Li Deng)and Tsinghua University(for Yang Liu), who provided excellent environments, supports, and encouragements that have been instrumental for us to complete this book. Yang Liu is also supported by National Natural Science Foundation of China(No. 61522204, No. 61432013, and No. 61331013 Seattle. usa g Beijing, China Yang liu October 2017 Contents 1 A Joint Introduction to Natural Language processing and to Deep learning Li Deng and Yang liu Deep learning in Conversational Language Understanding 23 Gokhan Tur, Asli Celikyilmaz, Xiaodong He, Dilek Hakkani-Tur and Li Deng 3 Deep Learning in Spoken and Text-Based Dialog Systems Asli Celikyilmaz, Li Deng and Dilek Hakkani-Tur 4 Deep Learning in Lexical Analysis and Parsing 79 Wanxiang Che and Yue Zhang 5 Deep Learning in Knowledge Graph 117 Zhiyuan Liu and Xianpei Han 6 Deep Learning in Machine Translation 147 Yang Liu and Jiajun Zhang 7 Deep learning in Question Answering 185 Kang liu and yansong feng 8 Deep Learning in Sentiment Analysis .219 Duyu Tang and meishan Zhang 9 Deep learning in Social Computing 255 Xin Zhao and Chenliang li 10 Deep learning in Natural Language Generation from Images 89 Xiaodong He and Li Deng 11 Epilogue: Frontiers of NLP in the Deep learning era 309 Li deng and Y ang Liu Glossary 327

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qq_38874510 可惜没有中文版
2018-11-18
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