Improving Query Expansion by Automatic Query Disambiguation in Intelligent Information Retrieval

Abstract

We study in this paper the impact of Word Sense Disambiguation (WSD) on Query Expansion (QE) for monolingual intelligent information retrieval. The proposed approaches for WSD and QE are based on corpus analysis using co-occurrence graphs modelled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantages of a double measure (possibility and necessity). Our experiments are performed using the standard ROMANSEVAL test collection for the WSD task and the CLEF-2003 benchmark for the QE process in French monolingual Information Retrieval (IR) evaluation. The results show the positive impact of WSD on QE based on the recall/precision standard metrics.