Noun Sense Disambiguation Based on Semantic Density
Gongshen Liu · 2012
Proposed a novel approach for noun sense disambiguation based on concept correlation.Different from existing algorithms,we extended the notion of semantic distance on WordNet by defining a semantic density for a group of word senses,thus quantizing the correlation among a group of word senses.We disambiguated noun sense after converting the correlation into semantic density.Besides,we also proposed an LSH like semantic hashing on WordNet.With semantic hashing,we greatly reduced the time complexity of calculating semantic density and that of the whole disambiguation algorithm.Experiments and evaluation of this novel approach on SemCor were made.