Experiments on two Query Expansion Approaches for a Proximity-based Information Retrieval Model
Résumé
Query expansion is a well-known technique used to overcome the word-mismatch drawback of keyword retrieval models. Fully automated query expansion comes with the risk of query drift. In our work we faced this phenomenon while trying to expand boolean queries for a Proximity-based information retrieval model. This model gets good precision in evaluation campaigns but gives a small number of results. Our experiments are focused on two different query expansion approaches: a global approach using WordNet synonyms and a local approach using pseudo relevance feedback based on LSA (Latent Semantic Analysis) to create a query-time thesaurus. The results we've got show an important query drift effect for both approaches. In this paper we present these experiences with an analysis of the results and the perspectives we are currently working on.