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9783030789633: Time Expression and Named Entity Recognition

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ISBN 10 : 3030789632 ISBN 13 : 9783030789633

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Langue: anglais
ISBN 10: 3030789632
ISBN 13: 9783030789633
Reliure: Tapa blanda
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9783030789602: Time Expression and Named Entity Recognition

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ISBN 10 : 3030789608 ISBN 13 : 9783030789602
Editeur : Springer Nature Switzerland AG, 2021
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Time Expression and Named Entity Recognition

Xiaoshi Zhong|Erik Cambria

Edit� par Springer International Publishing, 2022

ISBN 10 : 3030789632 ISBN 13 : 9783030789633

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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a synthetic analysis about the characteristics of timexes and entitiesReports the latest findings on recognizing timexes and entities from unstructured textOpens a door to examine whether multiple joint tasks enhance each other und. N� de r�f. du vendeur 668445020

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Time Expression and Named Entity Recognition

Zhong, Xiaoshi; Cambria, Erik

Edit� par Springer, 2022

ISBN 10 : 3030789632 ISBN 13 : 9783030789633

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Etat : New. In. N� de r�f. du vendeur ria9783030789633_new

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Time Expression and Named Entity Recognition

Erik Cambria

Edit� par Springer International Publishing, 2022

ISBN 10 : 3030789632 ISBN 13 : 9783030789633

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use. N� de r�f. du vendeur 9783030789633

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Time Expression and Named Entity Recognition

Erik Cambria

Edit� par Springer International Publishing Aug 2022, 2022

ISBN 10 : 3030789632 ISBN 13 : 9783030789633

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use. 116 pp. Englisch. N� de r�f. du vendeur 9783030789633

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