• 1 Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]

The aim of this work is to use a hybrid approach to extract CVs' competences. The extraction approach for competences is made of two phases: a segmentation into sections phase within which the terms representing the competences are extracted from a CV; and a prediction phase that consists from the features previously extracted, to foretell a set of competences that would have been deduced and that would not have been necessary to mention in the resume of that expert. The main contributions of the work are two folds : the use of the approach of the hierarchical clustering of a résume in section before extracting the competences; the use of the multi-label learning model based on SVMs so as to foretell among a set of skills, those that we deduce during the reading of a CV. Experimentation carried out on a set of CVs collected from an internet source have shown that, more than 10% improvement in the identification of blocs compared to a model of the start of the art. The multi-label competences model of prediction allows finding the list of competences with a precision and a reminder respectively in an order of 90.5 % and 92.3 %. .