2012, ISBN: 363938735X

[EAN: 9783639387353], Neubuch, [PU: AV Akademikerverlag], nach der Bestellung gedruckt Neuware - Printed after ordering - Revision with unchanged content. Supervised word sense disambigua… Mehr…

[EAN: 9783639387353], Neubuch, [PU: AV Akademikerverlag], nach der Bestellung gedruckt Neuware - Printed after ordering - Revision with unchanged content. 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-the-art 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. 188 pp. Englisch, Books<

ISBN: 9783639387353

Revision with unchanged content. Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is difficult for machine learning, mainly due to two probl… Mehr…

Revision with unchanged content. 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-the-art 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>MS Development>Windows Prog, KS Omniscriptum Publishing Core >1<

ISBN: 9783639387353

Revision with unchanged content. Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is difficult for machine learning, mainly due to two probl… Mehr…

Revision with unchanged content. 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-the-art 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. Bücher / Naturwissenschaften, Medizin, Informatik & Technik / Informatik & EDV / Informatik, [PU: VDM Verlag Dr. Müller, Saarbrücken]<

ISBN: 363938735X

Towards High-Performance Word Sense Disambiguation ab 68 EURO Combining Rich Linguistic Knowledge and Machine Learning Approaches Medien > Bücher, [PU: VDM Verlag Dr. Müller, Saarbrücken]

2012, ISBN: 363938735X

Kartoniert / Broschiert Informatik, mit Schutzumschlag 11, [PU:AV Akademikerverlag]