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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Liu, Zhiyuan, Lin, Yankai, Sun, Maosong
Format: E-Book
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature 2020
Springer
Springer Singapore Pte. Limited
Ausgabe:1
Schlagworte:
Artificial intelligence
Big Data
Computational and corpus linguistics
Computational Linguistics
Computer science
Computer Technology
Computing and Information Technology
Data mining
Data Mining and Knowledge Discovery
Deep Learning
Document Representation
Expert systems / knowledge-based systems
Knowledge Representation
Language and Linguistics
Language Arts
Linguistics
Machine Learning
Natural language & machine translation
Natural language and machine translation
Natural Language Processing
Natural language processing (Computer science)
Natural Language Processing (NLP)
Nonfiction
Open Access
Representation Learning
Word Representation
ISBN:9811555729, 9789811555725, 9789811555732, 9811555737
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:Electronic reproduction. Dordrecht: Springer, 2020. Requires the Libby app or a modern web browser.
ISBN:9811555729
9789811555725
9789811555732
9811555737
DOI:10.1007/978-981-15-5573-2