Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV 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.As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book. 544 pp. Englisch. N� de ref. del art�culo: 9789819916023

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Representation Learning for Natural Language Processing

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Publicado por Springer Nature Singapore, 2023

ISBN 10: 981991602X ISBN 13: 9789819916023

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Condici�n: Hervorragend. Zustand: Hervorragend | Seiten: 544 | Sprache: Englisch | Produktart: B�cher. N� de ref. del art�culo: 41556667/1

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Representation Learning for Natural Language Processing

Publicado por Springer, 2023

ISBN 10: 981991602X ISBN 13: 9789819916023

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Condici�n: New. 2nd ed. 2023 edition NO-PA16APR2015-KAP. N� de ref. del art�culo: 26396348753

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Representation Learning for Natural Language Processing

Publicado por Springer, 2023

ISBN 10: 981991602X ISBN 13: 9789819916023

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Publicado por Springer, 2023

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Representation Learning for Natural Language Processing

Zhiyuan Liu

Publicado por Springer Nature Singapore, Springer Nature Singapore, 2023

ISBN 10: 981991602X ISBN 13: 9789819916023

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Taschenbuch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV 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, socialnetwork 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.As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book. N� de ref. del art�culo: 9789819916023

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Representation Learning for Natural Language Processing

Liu, Zhiyuan|Lin, Yankai|Sun, Maosong

Publicado por Springer, Berlin|Springer Nature Singapore|Tsinghua University|Springer, 2023

ISBN 10: 981991602X ISBN 13: 9789819916023

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Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part . N� de ref. del art�culo: 829432227

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