Electronic Resource

Representation Learning for Natural Language Processing

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.

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Nature Singapore., 2020

Collation

XXIV, 334 hlm; ill., lamp.,

Language

English

ISBN/ISSN

9789811555732

Classification

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Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Artificial Intelligence
Data Mining and Knowledge Discovery
Natural Language Processing (NLP)
Computational Linguistics

Natural Language Processing (NLP

Specific Detail Info

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.

Statement of Responsibility

Zhiyuan Liu , Yankai Lin , Maosong Sun

Other Information

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Source

https://link.springer.com/book/10.1007/978-981-15-5573-2

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-981-15-5573-2

Journal Volume

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Journal Issue

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