Un modèle de langue pour l'estimation de la fraîcheur et la pertinence des documents Web

An effective information retrieval system must satisfy different users search intentions expecting a variety of query categories, comprising recency sensitive queries where fresh content is the major user’s requirement. However, using temporal features of documents to measure their freshness remains a hard task since these features may not be accurately represented in recent documents. In this paper, we propose a language model which estimates the topical relevance and freshness of documents with respect to real-time sensitive queries. In order to improve freshness ranking, our approach models freshness by exploiting terms extracted from recently posted tweets topically relevant to each real-time sensible queries. Our experiments show that there is a clear advantage of using twitter to extract fresh keywords. MOTS-CLÉS : Requêtes sensibles au temps, Modèles de langue, Mots-clés frais.