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Why do Successful Search Systems Fail for Some Topics
Auteur(s)
Date de parution
2007
In
Proceedings of the 2007 ACM Symposium on Applied Computing (SAC’07), Association for Computing Machinery (ACM), 2007///872-877
Résumé
This paper describes and evaluates the vector-space and probabil-istic IR models used to retrieve news articles from a corpus writ-ten in the French language. Based on three CLEF test-collections and 151 queries, we classify the poor retrieval results of difficult topics under 6 categories. The explanations we obtain from this analysis differ from those suggested a priori by our students. We use the Web to manually or automatically find related search terms to the original query. We evaluate these two query expan-sion strategies in order to improve mean average precision (MAP) and to reduce the number of topics for which no pertinent re-sponses are listed among the top ten references returned.
Identifiants
Type de publication
journal article
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