Thésaurus distributionnels pour la recherche d'information et vice-versa

Distributional thesauri are useful in many tasks of Natural Language Processing. In this paper, we address the problem of building and evaluating such thesauri with the help of Information Retrieval concepts. Two main contributions are proposed. First, in the continuation of the work of (Claveau et al., 2014), we show how IR tools and concepts can be used with success to build thesaurus. Through several experiments and by evaluating directly the results with reference lexicons, we show that some IR models outperform state-of-the-art systems. Secondly, we use IR as an applicative framework to indirectly evaluate the generated thesaurus. Here again, this task-based evaluation validate the IR approach used to build the thesaurus. Moreover, it allows us to compare these results with those from the direct evaluation framework used in the literature. The observed differences question these evaluation habits. MOTS-CLÉS : thésaurus distributionnels, sémantique distributionnelle, construction de lexique, modèles de RI, évaluation directe, évaluation par tâche, extension de requêtes.