Query clustering and IR system detection: experiments on TREC data

Authors:
Desire Kompaore;Josiane Mothe;Alain Baccini;Sebastien Dejean
Affiliations:
Institut de Recherche en Informatique de Toulouse, IRIT, France;Institut de Recherche en Informatique de Toulouse, IRIT, France;Universite Paul Sabatier, Toulouse cedex, France;Universite Paul Sabatier, Toulouse cedex, France
Venue:
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Year:
2007

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Abstract

This paper investigates two aspects in this experiment. Linguistic techniques are used to categorize queries in a first step. This classification is then used to analyze systems performances in a TREC context. More precisely, we cluster TREC topics with 13 linguistic features (Mothe and al, 2005), and use the systems which have participated to TREC3, 5, 6, and 7 campaign. The results show that our method can improve the results of the retrieval process compared to CombMNZ technique (Lee, 1997) and the best systems of each TREC campaign. When evaluated on a training/testing mode, we obtain an improvement, depending on the years considered, from 3.72% to 5.97% for P@5 and from 1.48% to 6.73% for P@10.