Classification Supervisée de Questions : Rôle de l'Expansion Sémantique

Abstract

Responding correctly to a question given a large collection of textual data is not an easy task. There is a need to perceive and recognize the question at a level that permits to detect some constraints that the question imposes on possible answers. The question classification task is used in Question Answering systems. This deduces the type of the expected answer, to perform a semantic classification to the target answer. The purpose is to provide additional information to reduce the gap between answer and question. An approach to improve the effectiveness of classifiers focusing on linguistic analysis and statistical approaches. This work also proposes two methods of questions expansion. Various questions representation, term weighting and diverse machine learning algorithms are studied. Experiments conducted on actual data are presented. Of interest is the improvement in the precision on the classification of questions. MOTS-CLES : Classification, Selections des descripteurs, Expansion semantique, Apprentissage, Fouille de texte.