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Representation Learning Natural Language by Liu Zhiyuan (36 results)
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Representation Learning for Natural Language Processing
Published by Springer (edition 1st ed. 2020), 2020
ISBN 10: 9811555729 ISBN 13: 9789811555725
Language: English
Hardcover. Condition: Very Good. 1st ed. 2020. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
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Representation Learning for Natural Language Processing
First Edition
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Representation Learning for Natural Language Processing
Published by Springer (edition 2nd ed. 2023), 2023
ISBN 10: 9819915996 ISBN 13: 9789819915996
Language: English
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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
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Representation Learning for Natural Language Processing
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Representation Learning for Natural Language Processing
Published by Springer Nature Singapore, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
Language: English
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Add to basketCondition: Hervorragend. Zustand: Hervorragend | Seiten: 544 | Sprache: Englisch | Produktart: Bücher.
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Condition: New. 2nd ed. 2023 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
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Representation Learning for Natural Language Processing
Published by Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10: 9811555753 ISBN 13: 9789811555756
Language: English
US$ 55.16
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Add to basketTaschenbuch. 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.
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Representation Learning for Natural Language Processing
Published by Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
Language: English
US$ 55.16
Convert currencyUS$ 35.79 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: 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.
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US$ 80.99
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Add to baskethardcover. Condition: New. 2nd ed. 2023. Special order direct from the distributor.
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Representation Learning for Natural Language Processing
Published by Springer-Nature New York Inc, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
Language: English
US$ 87.96
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Add to basketPaperback. Condition: Brand New. 2nd edition. 541 pages. 9.25x6.10x1.10 inches. In Stock.
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Representation Learning for Natural Language Processing
Published by Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 9819915996 ISBN 13: 9789819915996
Language: English
US$ 69.54
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Add to basketBuch. Condition: 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.
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Representation Learning for Natural Language Processing
Published by Springer-Nature New York Inc, 2023
ISBN 10: 9819915996 ISBN 13: 9789819915996
Language: English
US$ 105.95
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Add to basketHardcover. Condition: Brand New. 2nd edition. 541 pages. 9.25x6.10x1.34 inches. In Stock.
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US$ 99.86
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Representation Learning for Natural Language Processing
Published by Springer Nature Singapore Aug 2023, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 49.20
Convert currencyUS$ 25.68 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: 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.
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Representation Learning for Natural Language Processing
Published by Springer Nature Singapore Sep 2020, 2020
ISBN 10: 9811555753 ISBN 13: 9789811555756
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 49.20
Convert currencyUS$ 25.68 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 360 pp. Englisch.
