A Hybrid Distributional and Knowledge-based Model of Lexical Semantics
نویسندگان
چکیده
A range of approaches to the representation of lexical semantics have been explored within Computational Linguistics. Two of the most popular are distributional and knowledgebased models. This paper proposes hybrid models of lexical semantics that combine the advantages of these two approaches. Our models provide robust representations of synonymous words derived from WordNet. We also make use of WordNet’s hierarcy to refine the synset vectors. The models are evaluated on two widely explored tasks involving lexical semantics: lexical similarity and Word Sense Disambiguation. The hybrid models are found to perform better than standard distributional models and have the additional benefit of modelling polysemy.
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