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Representation Learning for Natural Language Processing
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Representation Learning for Natural Language Processing
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Representation Learning for Natural Language Processing
Language: English
Published by Springer (edition 1st ed. 2020), 2020
ISBN 10: 9811555729 ISBN 13: 9789811555725
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Representation Learning for Natural Language Processing
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Representation Learning for Natural Language Processing
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Representation Learning for Natural Language Processing
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Condition: New. pp. XXIV, 334 131 illus., 99 illus. in color. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Representation Learning for Natural Language Processing
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Representation Learning for Natural Language Processing
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Representation Learning for Natural Language Processing
Language: English
Published by Springer-Nature New York Inc, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
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Representation Learning for Natural Language Processing
Language: English
Published by Springer-Nature New York Inc, 2023
ISBN 10: 9819915996 ISBN 13: 9789819915996
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Taschenbuch. Condition: Neu. Representation Learning for Natural Language Processing | Zhiyuan Liu (u. a.) | Taschenbuch | xx | Englisch | 2023 | Springer | EAN 9789819916023 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Representation Learning for Natural Language Processing | Zhiyuan Liu (u. a.) | Taschenbuch | xxiv | Englisch | 2020 | Springer | EAN 9789811555756 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
