Expansion sélective de requêtes par apprentissage

Query expansion (QE) improves the retrieval quality in average, even though it can dramatically decrease performance for certain queries. This observation drives the trend to suggest selective approaches that aim at choosing the best function to apply for each query. Most of selective approaches use a learning process on past query features and results. This paper presents a new selective QE method that relies on query difficulty predictors. The method combines statistically and linguistically based predictors. The QE method is learned by a SVM. We demonstrate the efficiency of the proposed method on a number of standard TREC benchmarks. The supervised learning models have performed the query classification with more than 90% accuracy on the test collection. Our approach improves MAP by more than 11%, compared to the non selective methods. MOTS-CLÉS : Recherche sélective d’information, Prédicteurs de difficulté, Difficulté de requête, Expansion de requête, Apprentissage.