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Filtering pages about an entity (person, company, ...) so that only documents being of interest are kept is a real challenge. The interest can be qualified using criteria such as recency, novelty. In the last decade, we have seen classification systems trained to detect the interest for a document regarding an entity. Some approaches strive to build entity independent classification systems. Those approaches obtain good performances, but we show that they can be improved. The entities may differ on certain aspects that we think can be caught using clustering. Thus, instead of having one model per entity or one model for all entities, we propose an approach that uses one model per cluster of entities. We also introduce different strategies for automatic classification model selection. In this article, we detail the different aspects of our approach and we test it on the Knowledge Base Acceleration framework from the Text REtrieval Conference. We show that our approach brings significant improvements over a non-cluster based method. MOTS-CLES : regroupement d’entites, classification, detection d’informations importantes

Regroupement par popularité pour la RI semi-supervisée centrée sur les entités