Da: Books From California, Simi Valley, CA, U.S.A.
hardcover. Condizione: Very Good.
Da: Books From California, Simi Valley, CA, U.S.A.
hardcover. Condizione: Good. Book is bent.
EUR 45,15
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Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer (edition 1st ed. 2020), 2020
ISBN 10: 9811555729 ISBN 13: 9789811555725
Da: BooksRun, Philadelphia, PA, U.S.A.
Hardcover. Condizione: Very Good. 1st ed. 2020. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 48,03
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 64,83
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Condizione: New. 2nd ed. 2023 edition NO-PA16APR2015-KAP.
Condizione: New. pp. XXIV, 334 131 illus., 99 illus. in color. 1st ed. 2020 edition NO-PA16APR2015-KAP.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 52,84
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 60,15
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condizione: New. Representation Learning for Natural Language Processing. Book.
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EUR 60,13
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Lingua: Inglese
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
Da: Revaluation Books, Exeter, Regno Unito
EUR 75,93
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Aggiungi al carrelloPaperback. Condizione: Brand New. 2nd edition. 541 pages. 9.25x6.10x1.10 inches. In Stock.
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 9819915996 ISBN 13: 9789819915996
Da: Revaluation Books, Exeter, Regno Unito
EUR 91,47
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 87,07
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Aggiungi al carrelloPaperback. Condizione: New. New. book.
Lingua: Inglese
Editore: Springer, Springer Nature Singapore, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 49,09
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Springer, Springer Nature Singapore, 2023
ISBN 10: 9819915996 ISBN 13: 9789819915996
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 61,89
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: 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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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Seiten: 544 | Sprache: Englisch | Produktart: Bücher | 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.