Building a sense tagged corpus with open mind word expert
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
The Representation of Polysemy: MEG Evidence
Journal of Cognitive Neuroscience
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Criteria for the manual grouping of verb senses
LAW '07 Proceedings of the Linguistic Annotation Workshop
What is word meaning, really?: (and how can distributional models help us describe it?)
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
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Supervised word sense disambiguation requires training corpora that have been tagged with word senses, which begs the question of which word senses to tag with. The default choice has been WordNet, with its broad coverage and easy accessibility. However, concerns have been raised about the appropriateness of its fine-grained word senses for WSD. WSD systems have been far more successful in distinguishing coarsegrained senses than fine-grained ones (Navigli, 2006), but does that approach neglect necessary meaning differences? Recent psycholinguistic evidence seems to indicate that closely related word senses may be represented in the mental lexicon much like a single sense, whereas distantly related senses may be represented more like discrete entities. These results suggest that, for the purposes of WSD, closely related word senses can be clustered together into a more general sense with little meaning loss. The current paper will describe this psycholinguistic research and its implications for automatic word sense disambiguation.