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Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Livre
Etat : New.
Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : booksXpress, Bayonne, NJ, Etats-Unis
Livre
Hardcover. Etat : new.
Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Livre
Etat : As New. Unread book in perfect condition.
Edité par Springer Nature Switzerland Feb 2024, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Livre impression à la demande
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Empirical - data-driven, neural network-based, probabilistic, and statistical - methods seem to be the modern trend. Recently, OpenAI's ChatGPT, Google's Bard and Microsoft's Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars.Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers.The first part of the volume is dedicated to showing how carefully handcrafted linguistic resources could be successfully used to enhance current NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented simply because there is not enough data available for low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software, which is arguably the weakest point of NLP applications today but has a simple and elegant linguistic solution.It is the editor's belief that readers interested in Natural Language Processing will appreciate the importance of this volume, both for its questioning of the training corpus-based approaches and for the intrinsic value of the linguistic formalization and the underlying methodology presented. 240 pp. Englisch.
Edité par Springer Nature Switzerland, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : moluna, Greven, Allemagne
Livre impression à la demande
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Addresses the topic of multiword units in NLP software and the issue low-resource languagesDiscusses training corpus-based approaches and explains the intrinsic value of linguistic formalizationShows how carefully handcrafted linguistic res.
Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Livre
Etat : New.
Edité par Springer Nature Switzerland, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Livre
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Empirical - data-driven, neural network-based, probabilistic, and statistical - methods seem to be the modern trend. Recently, OpenAI's ChatGPT, Google's Bard and Microsoft's Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars.Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers.The first part of the volume is dedicated to showing how carefully handcrafted linguistic resources could be successfully used to enhance current NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented simply because there is not enough data available for low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software, which is arguably the weakest point of NLP applications today but has a simple and elegant linguistic solution.It is the editor's belief that readers interested in Natural Language Processing will appreciate the importance of this volume, both for its questioning of the training corpus-based approaches and for the intrinsic value of the linguistic formalization and the underlying methodology presented.
Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Livre
Etat : As New. Unread book in perfect condition.
Edité par Springer International Publishing AG, Cham, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : Grand Eagle Retail, Wilmington, DE, Etats-Unis
Livre Edition originale
Hardcover. Etat : new. Hardcover. Empirical data-driven, neural network-based, probabilistic, and statistical methods seem to be the modern trend. Recently, OpenAIs ChatGPT, Googles Bard and Microsofts Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars.Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers.The first part of the volume is dedicated to showing how carefully handcrafted linguistic resources could be successfully used to enhance current NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented simply because there is not enough data available for low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software, which is arguably the weakest point of NLP applications today but has a simple and elegant linguistic solution.It is the editor's belief that readers interested in Natural Language Processing will appreciate the importance of this volume, both for its questioning of the training corpus-based approaches and for the intrinsic value of the linguistic formalization and the underlying methodology presented. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : California Books, Miami, FL, Etats-Unis
Livre
Etat : New.
Edité par Springer-Nature New York Inc, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Livre
Hardcover. Etat : Brand New. 239 pages. 9.25x6.10x9.21 inches. In Stock.
Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Livre
Etat : New.
Edité par Springer International Publishing AG, Cham, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Livre Edition originale
Hardcover. Etat : new. Hardcover. Empirical data-driven, neural network-based, probabilistic, and statistical methods seem to be the modern trend. Recently, OpenAIs ChatGPT, Googles Bard and Microsofts Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars.Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers.The first part of the volume is dedicated to showing how carefully handcrafted linguistic resources could be successfully used to enhance current NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented simply because there is not enough data available for low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software, which is arguably the weakest point of NLP applications today but has a simple and elegant linguistic solution.It is the editor's belief that readers interested in Natural Language Processing will appreciate the importance of this volume, both for its questioning of the training corpus-based approaches and for the intrinsic value of the linguistic formalization and the underlying methodology presented. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Edité par Springer, 2024
ISBN 10 : 3031438108ISBN 13 : 9783031438103
Vendeur : Kennys Bookstore, Olney, MD, Etats-Unis
Livre
Etat : New.