WORD SENSE DISAMBIGUATION USING WORD ONTOLOGY AND CONCEPT DISTRIBUTION

This paper presents a method of word sense disambiguation that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to "literally" and "regularly" express a "concept". We apply set algebra to Wordnet's synsets cooperating with Wordnet's word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in Wordnet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts - we facilitate "concept distribution statistics" to determine the degree of semantic relatedness between two lexically expressed concepts. Human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially "ambiguous". The process of "deciding which of several meanings of a term is intended in a given context" is known as "Word Sense Disambiguation (WSD)". The proposed method is not supervised, and does not require any manually created sense-tagged training examples. The experimental results showed good performance on Semcor, a subset of the Brown corpus. We observe that measures of semantic relatedness are useful sources of information for word sense disambiguation.