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Representation Learning for Natural Language Processing

Liu Zhiyuan Lin, Yankai Sun Maosong
Heftet / 2020 / Engelsk

Produktdetaljer

ISBN
9789811555756
Publisert
2020-09-18
Utgiver
Vendor
Springer Verlag, Singapore
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet

Forfatter
Liu Zhiyuan
Lin, Yankai
Sun Maosong

Om bidragsyterne

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.

Representation Learning for Natural Language Processing

Liu Zhiyuan Lin, Yankai Sun Maosong
Heftet / 2020 / Engelsk
Liu Zhiyuan Lin, Yankai Sun Maosong
Heftet / 2020 / Engelsk
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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 andgraduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Les mer
1. ​Representation Learning and NLP.- 2. Word Representation.- 3. Compositional Semantics.- 4. Sentence Representation.- 5. Document Representation.- 6. Sememe Knowledge Representation.- 7. World Knowledge Representation.- 8. Network Representation.- 9. Cross-Modal Representation.- 10. Resources.- 11. Outlook.
Les mer
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.
Les mer
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.
Les mer
Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Les mer

Relaterte produkter

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 andgraduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Les mer
1. ​Representation Learning and NLP.- 2. Word Representation.- 3. Compositional Semantics.- 4. Sentence Representation.- 5. Document Representation.- 6. Sememe Knowledge Representation.- 7. World Knowledge Representation.- 8. Network Representation.- 9. Cross-Modal Representation.- 10. Resources.- 11. Outlook.
Les mer
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.
Les mer
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.
Les mer
Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Les mer

Produktdetaljer

ISBN
9789811555756
Publisert
2020-09-18
Utgiver
Vendor
Springer Verlag, Singapore
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet

Forfatter
Liu Zhiyuan
Lin, Yankai
Sun Maosong

Om bidragsyterne

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.

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