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Representation learning for natural ...

Lin, Yankai.

Representation learning for natural language processing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Representation learning for natural language processingby Zhiyuan Liu, Yankai Lin, Maosong Sun.
Author:	Liu, Zhiyuan.
other author:	Lin, Yankai.
Published:	Singapore :Springer Singapore :2020.
Description:	xxiv, 334 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science)
Online resource:	https://doi.org/10.1007/978-981-15-5573-2
ISBN:	9789811555732$q(electronic bk.)

Representation learning for natural language processing
Liu, Zhiyuan.

Representation learning for natural language processing[electronic resource] /by Zhiyuan Liu, Yankai Lin, Maosong Sun. - Singapore :Springer Singapore :2020. - xxiv, 334 p. :ill., digital ;24 cm.

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.

Open access.

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.

ISBN: 9789811555732$q(electronic bk.)

Standard No.: 10.1007/978-981-15-5573-2doiSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Representation learning for natural language processing
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505 0 $a 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.
506 $a Open access.
520 $a 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.
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