Choosing Sense Distinctions for WSD: Psycholinguistic Evidence

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.