Improving Query Expansion by Automatic Query Disambiguation in Intelligent Information Retrieval

Abstract

In this research, we investigate how Word Sense Disambiguation (WSD) affects Query Expansion (QE) in the context of
intelligent information retrieval for monolingual languages. Corpus analysis supported by possibilistic network-modeled co-occurrence
graphs is the basis of the WSD and QE suggested methods. In fact, our relevance judgment model makes use of a
dual measure—possibility and necessity—by way of possibility theory. When conducting our experiments, we make use of the
CLEF-2003 benchmark for the QE process in French monolingual Information Retrieval (IR) assessment and the standard
ROMANSEVAL test collection for the WSD job. According to the data, WSD has a beneficial effect on QE when measured
using the usual metrics of recall and accuracy.