Linguistic Resources for Natural Language Processing: On the Necessity of Using Linguistic Methods to Develop NLP Software: 9783031438103

Descripci�n Buch. Condici�n: 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. N� de ref. del art�culo: 9783031438103

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Linguistic Resources for Natural Language Processing : On the Necessity of Using Linguistic Methods to Develop Nlp Software

Silberztein, Max (EDT)

Publicado por Springer (2024)

ISBN 10: 3031438108 ISBN 13: 9783031438103

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Descripci�n Condici�n: New. N� de ref. del art�culo: 46854761-n

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Linguistic Resources for Natural Language Processing

Publicado por Springer Nature Switzerland (2024)

ISBN 10: 3031438108 ISBN 13: 9783031438103

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Descripci�n Condici�n: 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. N� de ref. del art�culo: 1027290483

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Linguistic Resources for Natural Language Processing : On the Necessity of Using Linguistic Methods to Develop NLP Software

Max Silberztein

Publicado por Springer Nature Switzerland (2024)

ISBN 10: 3031438108 ISBN 13: 9783031438103

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Descripci�n Buch. Condici�n: 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. N� de ref. del art�culo: 9783031438103

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Linguistic Resources for Natural Language Processing (Hardcover)

Max Silberztein

Publicado por Springer International Publishing AG, Cham (2024)

ISBN 10: 3031438108 ISBN 13: 9783031438103

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Descripci�n Hardcover. Condici�n: 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. N� de ref. del art�culo: 9783031438103

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