In this paper we propose a method for semantic text representation and term weighting. It is based on a semantic resource, WordNet, that provides meaning information and relations between the terms of a document. The heart of the proposed method is the way the concepts (terms) of documents are clustered and weighted. More precisely, we introduce two notions: the centrality of a term and its specificity. The centrality of a term is given by the number of terms of the document that are directly related to it in the same conceptual cluster. The specificity represents the depth of a concept in WordNet. These parameters are different from the usual term frequency tf and inverse term frequency idf used in classical information retrieval. This method is based on two steps: 1) matching document terms with concepts of WordNet in order to obtain the most appropriate ones 2) for each concept calculating its centrality using existing semantic WordNet relations, and its specificity. The preliminary experiments undertaken on TREC collections show the effective interest of these parameters.