An Intelligent Information Retrieval System using Automatic Word Sense Disambiguation

Prasanna G. Ramasubramanian, Arvin Agah and Susan E. Gauch

Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS 66045 USA.

Abstract

This Paper aims to establish that an intelligent contextual information retrieval (IR) system can improve the quality of search results by retrieving more relevant results than those obtained with traditional search engines. Search engines capable of implicit, explicit and no contextual retrieval were designed and implemented and their performances studied. Experimental results show that search engines with contextual IR produce results that are more relevant, and the outcomes further indicate that there is no perceived gain in choosing specifically any any one of the two approaches of implicit or explicit. The performance of the indexing mechanism, as it classifies document tokens with their appropriate contexts/word sense, was evaluated. The effectiveness if the word sense disambiguation process was found to depend to a great extent on the process (implementation) as well as the raw data (thesaurus)

Keywords

icontextual information retrieval, word sense disambiguation