Read While You Wait - Get immediate ebook access, if available*, when you order a print book

Open Access This content is freely available online to anyone, anywhere at any time.

Representation Learning for Natural Language Processing

Authors: Liu, Zhiyuan, Lin, Yankai, Sun, Maosong

Free Preview
  • Open Access
  • Provides a comprehensive overview of the representation learning techniques for natural language processing.
  • Presents a systematic and thorough introduction to the theory, algorithms and applications of representation learning.
  • Shares insights into the future research directions for each topic as well as for the overall field of representation learning for natural language processing.
see more benefits

Buy this book

eBook  
  • ISBN 978-981-15-5573-2
  • This book is an open access book, you can download it for free on link.springer.com
Hardcover $59.99
price for Brazil
  • ISBN 978-981-15-5572-5
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
About this book

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions.

The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

About the authors

Zhiyuan Liu is an Associate Professor at the Department of Computer Science and Technology at Tsinghua University, China. His research interests include representation learning, knowledge graphs and social computation, and he has published more than 80 papers in at leading conferences and in respected journals. He has received several awards/honors, including Excellent Doctoral Dissertation awards from Tsinghua University and the Chinese Association for Artificial Intelligence, and was named as one of  MIT Technology Review Innovators Under 35 China (MIT TR-35 China). He has served as area chair for various conferences, including ACL, EMNLP, COLING.

Yankai Lin is a researcher at the Pattern Recognition Center, Tencent Wechat. He received his Ph.D. degree in Computer Science from Tsinghua in 2019. His research interests include representation learning, information extraction and question answering. He has published more than 10 papers at international conferences, including ACL, EMNLP, IJCAI and AAAI. He was named an Academic Rising Star of Tsinghua University and a Baidu Scholar.

Maosong Sun is a Professor at the Department of Computer Science and Technology and the Executive Vice Dean of the Institute for Artificial Intelligence, Tsinghua University. His research interests include natural language processing, machine learning, computational humanities and social sciences. He is the chief scientist of the National Key Basic Research and Development Program (973 Program) and the chief expert of various major National Social Science Fund of China projects. He has published over 100 papers at leading conferences and in respected journals. He is the Director of Tsinghua University-National University of Singapore Joint Research Center on Next Generation Search Technologies, and the editor-in-chief of the Journal of Chinese Information Processing. He received the Nationwide Distinguished Practitioner award from the State Commission for Language Affairs, People’s Republic of China, in 2007, and the National Excellent Scientific and Technological Practitioner award from the China Association for Science and Technology in 2016.

Table of contents (11 chapters)

Table of contents (11 chapters)
  • Representation Learning and NLP

    Pages 1-11

    Liu, Zhiyuan (et al.)

  • Word Representation

    Pages 13-41

    Liu, Zhiyuan (et al.)

  • Compositional Semantics

    Pages 43-57

    Liu, Zhiyuan (et al.)

  • Sentence Representation

    Pages 59-89

    Liu, Zhiyuan (et al.)

  • Document Representation

    Pages 91-123

    Liu, Zhiyuan (et al.)

Buy this book

eBook  
  • ISBN 978-981-15-5573-2
  • This book is an open access book, you can download it for free on link.springer.com
Hardcover $59.99
price for Brazil
  • ISBN 978-981-15-5572-5
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Representation Learning for Natural Language Processing
Authors
Copyright
2020
Publisher
Springer Singapore
Copyright Holder
The Editor(s) (if applicable) and The Author(s)
eBook ISBN
978-981-15-5573-2
DOI
10.1007/978-981-15-5573-2
Hardcover ISBN
978-981-15-5572-5
Edition Number
1
Number of Pages
XXIV, 334
Number of Illustrations
32 b/w illustrations, 99 illustrations in colour
Topics

*immediately available upon purchase as print book shipments may be delayed due to the COVID-19 crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works are not included.