A00-3007 retrieval precision through word sense disambiguation . An evaluation against human
A00-3007 . We hope to verify that word sense disambiguation leads to improved precision in
A88-1020 were ignored in the new sprout . Sense disambiguation did not significantly improve
A00-2019 detection as an extension of the word sense disambiguation ( WSD ) problem . Corpus-based
A00-2009 Conclusions This paper shows that word sense disambiguation accuracy can be improved by combining
A88-1020 reached closure at the 11th level . Sense Disambiguation Perhaps the diversity in meaning
A00-2009 Gale et al. , 1992 ) , where word sense disambiguation is performed with a Naive Bayesian
A00-1037 coreference resolution , word sense disambiguation , and others have to be dealt
A00-1003 considerably reduces the word sense disambiguation problem . However , some ambiguity
A88-1020 methods of sense marking ( i.e. sense disambiguation ) : disambiguation by symmetry
A00-3007 literature , evaluation of word sense disambiguation systems is not yet standardized
A00-3007 Abstract We have developed a word sense disambiguation algorithm , following Cheng and
A00-2034 alternations performed better on a word sense disambiguation task compared to preferences
A00-1012 successful when applied to the word sense disambiguation problem ( Stevenson and Wilks
A00-2011 of duty . While previous word sense disambiguation algorithms rely on a lexicon
A00-2009 Bayesian Classifiers for Word Sense Disambiguation </title> Ted Pedersen Abstract
A00-2009 corpus-based approach to word sense disambiguation that builds an ensemble of Naive
A00-3007 our efforts to develop a word sense disambiguation ( WSD ) algorithm aimed at improving
A00-2009 results . 1 Introduction Word sense disambiguation is often cast as a problem in
A00-3007 Encoding Initiative . <title> Word Sense Disambiguation for Cross-Language Information
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