representation learning natural language (24 Ergebnisse)

Zustand: Good. Ships from the UK. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

23,5 x 15,5 cm. Zustand: Gut. 2. Printing. XIII, 492 Seiten ; Mit Figuren Innen mit Legende, sauberer, guter Zustand. Hardcover, Pappeinband, mit den üblichen Bibliotheks-Markierungen, Stempeln und Einträgen, innen wie außen, siehe Bilder. (Evtl. auch Kleber- und/oder Etikettenreste, sowie -abdrücke durch abgelöste Bibliotheksschilder). Einband etwas fleckig. In Englisch RW-00-05E|S65 Sprache: Englisch Gewicht in Gramm: 933.

22,5 x 15 cm. Zustand: Gut. Volume 1. XIII, 492 Seiten ; Mit Figuren The Mit Press Series in Artificial Intelligence - Softcover, Broschur mit Gebrauchsspuren und mit den üblichen Bibliotheks-Markierungen, Stempeln und Einträgen, innen wie außen, siehe Bilder. (Evtl. auch Kleber- und/oder Etikettenreste, sowie -abdrücke durch abgelöste Bibliotheksschilder). Seiten mit vielen Bleistiftmarkierungen im Text und/oder am Textrand, wenige Seiten leicht Randrissig, fleckenfrei. In Englisch B09-04-06D|S41 Sprache: Englisch Gewicht in Gramm: 772.

Zustand: Good. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 544 | Sprache: Englisch | Produktart: Bücher.

Zustand: Good. Volume 1. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1050grams, ISBN:0262230968.

Zustand: Good. Volume 1. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Dust jacket in fair condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1050grams, ISBN:0262230968.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 544 | Sprache: Englisch | Produktart: Bücher.

Hardcover. Zustand: New. 2nd ed. 2023. Brand new item, never opened! Ship within 24hrs. APO/FPO addresses supported.

Taschenbuch. Zustand: Neu. 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, 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 544 pp. Englisch.

Hardcover. Zustand: Very Good. 1st ed. 2020. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.

hardcover. Zustand: Very Good.

hardcover. Zustand: Good. Book is bent.

Taschenbuch. Zustand: 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.

Taschenbuch. Zustand: 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.

fester Einband. 15,5, x 23,5 cm., 492 und 486 S. Leinen mit O-Schutzumschlag, dieser leicht berieben, beide Bände mit 1 kl. Namenszug a.d. flieg. Vorsatz. Kopfschnitt min angestaubt, ansonsten sehr gute Exemplare.

Zustand: New. In.

Zustand: New. In.

Buch. Zustand: Neu. 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, 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 544 pp. Englisch.

Hardcover. Zustand: Near Fine. Zustand des Schutzumschlags: very good. Second printing. 6 x 9 in. Red cloth with silver spine lettering. Condition is NEAR FINE ; like new on all points with no markings. DJ is VERY GOOD ; minor edge wear, slightly rubbed, very clean. Science. Stax.

Buch. Zustand: 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.

Zustand: New. In.

Paperback. Zustand: Brand New. 2nd edition. 541 pages. 9.25x6.10x1.10 inches. In Stock.

Hardcover. Zustand: Brand New. 2nd edition. 541 pages. 9.25x6.10x1.34 inches. In Stock.