A00-3007 improve retrieval precision through word sense disambiguation . An evaluation against human
A00-3007 performance . We hope to verify that word sense disambiguation leads to improved precision in
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
A00-2009 ( Gale et al. , 1992 ) , where word sense disambiguation is performed with a Naive Bayesian
A00-1037 such as coreference resolution , word sense disambiguation , and others have to be dealt
A00-3007 Encoding Initiative . <title> Word Sense Disambiguation for Cross-Language Information
A00-1003 tagging considerably reduces the word sense disambiguation problem . However , some ambiguity
A92-1018 described : phrase recognition ; word sense disambiguation ; and grammatical function assignment
A92-1018 applications here : phrase recognition ; word sense disambiguation ; and grammatical function assign
A00-3007 WSD literature , evaluation of word sense disambiguation systems is not yet standardized
A00-3007 Diekema 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 sense of duty . While previous word sense disambiguation algorithms rely on a lexicon
A00-2009 Naive Bayesian Classifiers for Word Sense Disambiguation </title> Ted Pedersen Abstract
A00-2009 presents a corpus-based approach to word sense disambiguation that builds an ensemble of Naive
A00-3007 stages1 of our efforts to develop a word sense disambiguation ( WSD ) algorithm aimed at improving
A00-2009 have been shown to be optimal for word sense disambiguation . 5.2 Co -- occurrence features
A00-1012 be expected when humans perform word sense disambiguation ( Fellbaum et al. , 1998 ) but
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