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This paper deals with document clustering in the context of strategic and competitive intelligence on the Web (medium-sized corpora). Our goal is to assist the user with the following tasks: 1. find topics within collected textual data; 2. put each document in one or more of these topics. We present a corpus linguistics approach, using shared nearest neighbors within a cooccurrence graph. The topics we build may overlap (i.e. share many words) and are not only set of words: using graph theory formalism, we are able to express subtle semantic relations between words within each topic. First results are quite satisfying.

Sous-graphes de cooccurrences pour la détection de thématiques dans un corpus de taille moyenne