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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 word...

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Zapisane w:
Główni autorzy: Liu, Zhiyuan, Lin, Yankai, Sun, Maosong
Format: Online
Język:angielski
Wydane: Springer Nature 2021
Hasła przedmiotowe:
Natural Language Processing (NLP)
Computational Linguistics
Artificial Intelligence
Data Mining and Knowledge Discovery
Open Access
Deep Learning
Representation Learning
Knowledge Representation
Word Representation
Document Representation
Big Data
Machine Learning
Natural Language Processing
Natural language & machine translation
Computational linguistics
Artificial intelligence
Data mining
Expert systems / knowledge-based systems
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation
thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
Dostęp online:ONIX_20200714_9789811555732_9
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Streszczenie: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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