Semantic distances for sets of senses and applications in word sense disambiguation

There has been an increasing interest both from the Information Retrieval community and the Data Mining community in investigating possible advantages of using Word Sense Disambiguation (WSD) for enhancing semantic information in the Information Retrieval and Data Mining process. Although contradictory results have been reported, there are strong indications that the use of WSD can contribute to the performance of IR and Data Mining algorithms. In this paper we propose two methods for calculating the semantic distance of a set of senses in a hierarchical thesaurus and utilize them for performing unsupervised WSD. Initial experiments have provided us with encouraging results.