Electronic Resource

Representation learning for natural language processing

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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 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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Detail Information

Series Title

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Call Number

006.35 LIU r

Publisher

Berlin, Germany : Springer Nature., 2020

Collation

xxiv, 334p.: ill.

Language

English

ISBN/ISSN

9789811555732

Classification

006.35

Content Type

text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Artificial intelligence
Data mining
Computational linguistics
Natural language and machine translation

Specific Detail Info

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Statement of Responsibility

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