Results 1 - 10 of 1,894

Analogical Word Sense Disambiguation

by David Barbella, Kenneth D. Forbus
"... Word sense disambiguation is an important problem in learning by reading. This paper introduces analogical word-sense disambiguation, which uses human-like analogical processing over structured, relational representations to perform word sense disambiguation. Cases are automatically constructed usin ..."
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Word sense disambiguation is an important problem in learning by reading. This paper introduces analogical word-sense disambiguation, which uses human-like analogical processing over structured, relational representations to perform word sense disambiguation. Cases are automatically constructed

Subjectivity Word Sense Disambiguation

by Cem Akkaya, Janyce Wiebe, Rada Mihalcea
"... This paper investigates a new task, subjectivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses. We provide empirical evidence that SWSD is more feasible than ..."
Abstract - Cited by 27 (2 self) - Add to MetaCart
This paper investigates a new task, subjectivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses. We provide empirical evidence that SWSD is more feasible

Soft Word Sense Disambiguation

by unknown authors
"... Abstract: Word sense disambiguation is a core problem in many tasks related to language processing. In this paper, we introduce the notion of soft word sense disambiguation which states that given a word, the sense disambiguation system should not commit to a particular sense, but rather, to a set o ..."
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completely probabilistic framework for word-sense disambiguation with a semi-supervised learning technique utilising WordNet. WordNet can be customized to a domain using corpora from that domain. This idea applied to question answering has been evaluated on TREC data and the results are promising.

ASTATISTICAL METHOD FOR WORD-SENSE DISAMBIGUATION BY

by Rebecca Bruce , 1995
"... \A Statistical Method for Word-Sense Disambiguation, " a dissertation prepared by Rebecca Bruce in partial ful llment of the requirements for the degree, Doctor of Philosophy, has been approved and accepted by the following: ..."
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\A Statistical Method for Word-Sense Disambiguation, " a dissertation prepared by Rebecca Bruce in partial ful llment of the requirements for the degree, Doctor of Philosophy, has been approved and accepted by the following:

Soft Word Sense Disambiguation

by Ganesh Ramakrishnan Prithviraj, Ganesh Ramakrishnan, B. P. Prithviraj, A. Deepa, Pushpak Bhattacharyya - Masaryk University Brno, Brno, Czech Republic , 2004
"... Word sense disambiguation is a core problem in many tasks related to language processing. In this paper, we introduce the notion of soft word sense disambiguation which states that given a word, the sense disambiguation system should not commit to a particular sense, but rather, to a set of sense ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
Word sense disambiguation is a core problem in many tasks related to language processing. In this paper, we introduce the notion of soft word sense disambiguation which states that given a word, the sense disambiguation system should not commit to a particular sense, but rather, to a set

Dutch Word Sense Disambiguation:

by Optimizing The Localness, Iris Hendrickx, Antal Van Den Bosch, V Eronique Hoste, Walter Daelemans
"... We describe a new version of the Dutch word sense disambiguation system trained and tested on a corrected version of the SENSEVAL-2 data. The system is an ensemble of word experts; each word expert is a memory-based classifier of which the parameters are automatically determined through cross ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
We describe a new version of the Dutch word sense disambiguation system trained and tested on a corrected version of the SENSEVAL-2 data. The system is an ensemble of word experts; each word expert is a memory-based classifier of which the parameters are automatically determined through

Word Sense Disambiguation

by Mukti Desai , Mrs Kiran Bhowmick
"... ABSTRACT: Ambiguity and human language have been tangled since the rise of philological communication. One of the long established problems of Natural Language Processing (NLP) is Word Sense Disambiguation (WSD). Researchers have been diligently trying to deal with this problem since the birth of M ..."
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ABSTRACT: Ambiguity and human language have been tangled since the rise of philological communication. One of the long established problems of Natural Language Processing (NLP) is Word Sense Disambiguation (WSD). Researchers have been diligently trying to deal with this problem since the birth

Word-Sense Disambiguation

by Using Statistical Models, Trained On Large Corpora, David Yarowsky
"... This paper describes a program that disambiguates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's categories serve as approximations of conceptual classes. The categories listed for a word in Roget's index tend to co ..."
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This paper describes a program that disambiguates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's categories serve as approximations of conceptual classes. The categories listed for a word in Roget's index tend

word-sense disambiguation

by Alfredo Maldonado-guerra, Martin Emms
"... First-order and second-order context representations: ..."
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First-order and second-order context representations:

Smoothing and Word Sense Disambiguation

by Eneko Agirre, David Martinez - IN PROCEEDINGS OF ESTAL - ESPAÑA FOR NATURAL LANGUAGE PROCESSING , 2004
"... This paper presents an algorithm to apply the smoothing techniques described in [1] to three different Machine Learning (ML) methods for Word Sense Disambiguation (WSD). The method to obtain better estimations for the features is explained step by step, and applied to n-way ambiguities. The results ..."
Abstract - Cited by 4 (3 self) - Add to MetaCart
This paper presents an algorithm to apply the smoothing techniques described in [1] to three different Machine Learning (ML) methods for Word Sense Disambiguation (WSD). The method to obtain better estimations for the features is explained step by step, and applied to n-way ambiguities. The results
Results 1 - 10 of 1,894