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Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning
Language: English
Published by Morgan & Claypool Publishers, 2020
ISBN 10: 1636390218 ISBN 13: 9781636390215
Condition: Fine. Used book that is in almost brand-new condition. May contain a remainder mark. Better World Books: Buy Books. Do Good.
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Condition: As New. Unread book in perfect condition.
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Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning (Synthesis Lectures on Human Language Technologies)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 50.43
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In English.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning
Language: English
Published by Springer International Publishing, 2020
ISBN 10: 3031010493 ISBN 13: 9783031010491
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.
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Embeddings in Natural Language Processing
Language: English
Published by Springer International Publishing Nov 2020, 2020
ISBN 10: 3031010493 ISBN 13: 9783031010491
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature. 176 pp. Englisch.
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Embeddings in Natural Language Processing
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2020
ISBN 10: 3031010493 ISBN 13: 9783031010491
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a c.
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Condition: New. Print on Demand pp. 176.
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Embeddings in Natural Language Processing
Language: English
Published by Springer International Publishing, Springer International Publishing Nov 2020, 2020
ISBN 10: 3031010493 ISBN 13: 9783031010491
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch.
