SemSim: Resources for Normalized Semantic Similarity Computation Using Lexical Networks

Abstract

We investigate the creation of corpora from web-harvested data following a scalable approach that has linear query complexity. Individual web queries are posed for a lexicon that includes thousands of nouns and the retrieved data are aggregated. A lexical network is constructed, in which the lexicon nouns are linked according to their context-based similarity. We introduce the notion of semantic neighborhoods, which are exploited for the computation of semantic similarity. Two types of normalization are proposed and evaluated on the semantic tasks of: (i) similarity judgement, and (ii) noun categorization and taxonomy creation. The created corpus along with a set of tools and noun similarities are made publicly available.

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