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Towards High-Performance Word Sense Disambiguation- Combining Rich Linguistic Knowledge and Machine Learning Approaches Jinying Chen Author - novo libro
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is difficult for machine learning, mainly due to two problems: the lack of sense-tagged… mais…
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is difficult for machine learning, mainly due to two problems: the lack of sense-tagged training data and the sparsity of the matrix of observed instances vs. features. At the same time, high accuracy is necessary for WSD to be beneficial for high-level applications, such as information retrieval, question answering, and machine translation. This work addresses the above two problems through combining rich linguistic knowledge and machine learning methods. First, it proposes and demonstrates empirically evidence that careful design and generation of linguistically motivated features help to alleviate the data sparseness inherent in WSD. A state-of-theart supervised system for verb sense disambiguation was introduced. Exploration in three specific aspects of feature generation was discussed and shown to elevate the system accuracy to top-level. It also shows the effectiveness of active learning in the creation of more labeled training data for supervised WSD - reducing the required training data by 1/2 to 3/4 when learning coarse-grained English verb senses. The book is addressed to researchers in Computer and Information Science and Computational Linguistics. Trade Books>Trade Paperback>Technology>Windows>Programming, KS Omniscriptum Publishing Core >1<
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Chen, Jinying: Towards High-Performance Word Sense Disambiguation: Combining Rich Linguistic Knowledge and Machine Learning Approaches - Livro de bolso
Chen, Jinying: Towards High-Performance Word Sense Disambiguation: Combining Rich Linguistic Knowledge and Machine Learning Approaches - Livro de bolso
Chen, Jinying: Towards High-Performance Word Sense Disambiguation: Combining Rich Linguistic Knowledge and Machine Learning Approaches - Livro de bolso
Chen, Jinying: Towards High-Performance Word Sense Disambiguation: Combining Rich Linguistic Knowledge and Machine Learning Approaches - Livro de bolso
184 Seiten Taschenbuch Sehr gepflegtes Gebraucht-/Antiquariatsexemplar. Zustand unter Berücksichtigung des Alters sehr gut. Tagesaktueller, sicherer und weltweiter Versand. Wir liefern gr… mais…
184 Seiten Taschenbuch Sehr gepflegtes Gebraucht-/Antiquariatsexemplar. Zustand unter Berücksichtigung des Alters sehr gut. Tagesaktueller, sicherer und weltweiter Versand. Wir liefern grundsätzlich mit beiliegender Rechnung. 342817.01 Versand D: 3,00 EUR IT-Ausbildung & -Berufe / Naturwissenschaften & Technik / Genres, [PU:Vdm Verlag Dr. Müller,]<
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Towards High-Performance Word Sense Disambiguation- Combining Rich Linguistic Knowledge and Machine Learning Approaches Jinying Chen Author - novo libro
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is difficult for machine learning, mainly due to two problems: the lack of sense-tagged… mais…
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is difficult for machine learning, mainly due to two problems: the lack of sense-tagged training data and the sparsity of the matrix of observed instances vs. features. At the same time, high accuracy is necessary for WSD to be beneficial for high-level applications, such as information retrieval, question answering, and machine translation. This work addresses the above two problems through combining rich linguistic knowledge and machine learning methods. First, it proposes and demonstrates empirically evidence that careful design and generation of linguistically motivated features help to alleviate the data sparseness inherent in WSD. A state-of-theart supervised system for verb sense disambiguation was introduced. Exploration in three specific aspects of feature generation was discussed and shown to elevate the system accuracy to top-level. It also shows the effectiveness of active learning in the creation of more labeled training data for supervised WSD - reducing the required training data by 1/2 to 3/4 when learning coarse-grained English verb senses. The book is addressed to researchers in Computer and Information Science and Computational Linguistics. Trade Books>Trade Paperback>Technology>Windows>Programming, KS Omniscriptum Publishing Core >1<
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Chen, Jinying: Towards High-Performance Word Sense Disambiguation: Combining Rich Linguistic Knowledge and Machine Learning Approaches - Livro de bolso
Chen, Jinying: Towards High-Performance Word Sense Disambiguation: Combining Rich Linguistic Knowledge and Machine Learning Approaches - Livro de bolso
Chen, Jinying: Towards High-Performance Word Sense Disambiguation: Combining Rich Linguistic Knowledge and Machine Learning Approaches - Livro de bolso
184 Seiten Taschenbuch Sehr gepflegtes Gebraucht-/Antiquariatsexemplar. Zustand unter Berücksichtigung des Alters sehr gut. Tagesaktueller, sicherer und weltweiter Versand. Wir liefern gr… mais…
184 Seiten Taschenbuch Sehr gepflegtes Gebraucht-/Antiquariatsexemplar. Zustand unter Berücksichtigung des Alters sehr gut. Tagesaktueller, sicherer und weltweiter Versand. Wir liefern grundsätzlich mit beiliegender Rechnung. 342817.01 Versand D: 3,00 EUR IT-Ausbildung & -Berufe / Naturwissenschaften & Technik / Genres, [PU:Vdm Verlag Dr. Müller,]<
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Dados bibliográficos do melhor livro correspondente
Supervised word sense disambiguation (WSD) for truly polysemous words (in
contrast to homonyms) is difficult for machine learning, mainly due to two
problems: the lack of sense-tagged training data and the sparsity of the matrix
of observed instances vs. features. At the same time, high accuracy is necessary
for WSD to be beneficial for high-level applications, such as information
retrieval, question answering, and machine translation. This work addresses
the above two problems through combining rich linguistic knowledge
and machine learning methods. First, it proposes and demonstrates empirically
evidence that careful design and generation of linguistically motivated
features help to alleviate the data sparseness inherent in WSD. A state-of-theart
supervised system for verb sense disambiguation was introduced. Exploration
in three specific aspects of feature generation was discussed and
shown to elevate the system accuracy to top-level. It also shows the effectiveness
of active learning in the creation of more labeled training data for supervised
WSD - reducing the required training data by 1/2 to 3/4 when learning
coarse-grained English verb senses. The book is addressed to researchers in
Computer and Information Science and Computational Linguistics.
Dados detalhados do livro - Towards High-Performance Word Sense Disambiguation- Combining Rich Linguistic Knowledge and Machine Learning Approaches Jinying Chen Author
EAN (ISBN-13): 9783836427517 ISBN (ISBN-10): 3836427516 Livro de bolso Ano de publicação: 2007 Editor/Editora: KS Omniscriptum Publishing Core >1 184 Páginas Peso: 0,359 kg Língua: eng/Englisch
Livro na base de dados desde 2007-11-14T15:22:34+00:00 (Lisbon) Página de detalhes modificada pela última vez em 2023-10-14T11:07:19+01:00 (Lisbon) Número ISBN/EAN: 9783836427517
Número ISBN - Ortografia alternativa: 3-8364-2751-6, 978-3-8364-2751-7 Ortografia alternativa e termos de pesquisa relacionados: Autor do livro: chen Título do livro: machine learning, word sense disambiguation, high performance
Dados da editora
Autor: Jinying Chen Título: Towards High-Performance Word Sense Disambiguation - Combining Rich Linguistic Knowledge and Machine Learning Approaches Editora: VDM Verlag Dr. Müller 184 Páginas Ano de publicação: 2007-11-12 Língua: Inglês 68,00 € (DE) 70,00 € (AT) 114,00 CHF (CH) Not available (reason unspecified)
BC; PB; Hardcover, Softcover / Informatik, EDV; Informatik und Informationstechnologie; Linguistics; Word Sense Disambiguation; Linguistically Motivated Features; Learning; Feature Engineering; Natural Language Processing
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