RETRACTION: A New WSD approach using word ontology and concept distribution

Abstract

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. Human languages have words that can mean different things in different contexts, and such words with multiple meanings are potentially `ambiguous'. `Word sense disambiguation' means the process of `deciding which of several meanings of a term is intended in a given context'. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to express a `concept' literally and regularly. We apply set algebra to Wordnet's synsets, cooperating with Wordnet's word ontology. 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. Thus, we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate `concept distribution statistics' to determine the semantic relatedness between two lexically expressed concepts. Our method does not require any training in advanced. 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.