Towards High-Performance Word Sense Disambiguation- Combining Rich Linguistic Knowledge and Machine Learning Approaches Jinying Chen Author - neues Buch
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… Mehr…
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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Towards High-Performance Word Sense Disambiguation- Combining Rich Linguistic Knowledge and Machine Learning Approaches Jinying Chen Author - neues Buch
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… Mehr…
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<
184 Seiten Taschenbuch Sehr gepflegtes Gebraucht-/Antiquariatsexemplar. Zustand unter Berücksichtigung des Alters sehr gut. Tagesaktueller, sicherer und weltweiter Versand. Wir liefern gr… Mehr…
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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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.
Detailangaben zum Buch - Towards High-Performance Word Sense Disambiguation- Combining Rich Linguistic Knowledge and Machine Learning Approaches Jinying Chen Author
Buch in der Datenbank seit 2007-11-14T16:22:34+01:00 (Zurich) Detailseite zuletzt geändert am 2023-10-14T12:07:19+02:00 (Zurich) ISBN/EAN: 9783836427517
ISBN - alternative Schreibweisen: 3-8364-2751-6, 978-3-8364-2751-7 Alternative Schreibweisen und verwandte Suchbegriffe: Autor des Buches: chen Titel des Buches: machine learning, word sense disambiguation, high performance
Daten vom Verlag:
Autor/in: Jinying Chen Titel: Towards High-Performance Word Sense Disambiguation - Combining Rich Linguistic Knowledge and Machine Learning Approaches Verlag: VDM Verlag Dr. Müller 184 Seiten Erscheinungsjahr: 2007-11-12 Sprache: Englisch 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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