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 various word forms in synset(s). Then we construct 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.

Then we applied the measure of semantic relatedness to the WSD task. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for word sense disambiguation.