Une mesure d'intérêt à base de surreprésentation pour l'extraction des motifs syntaxiques stylistiques

In this contribution, we present a computational stylistic study of the French classic literature texts based on a data-driven approach where discovering interesting linguistic patterns is done without any prior knowledge. We propose an objective measure capable of capturing and extracting meaningful stylistic syntactic patterns from a given author's work. Our hypothesis is based on the fact that the most relevant syntactic patterns should significantly reflect the author's stylistic choice and thus they should exhibit some kind of overrepresentation behavior controlled by the author's purpose. The analysed results show the effectiveness in extracting interesting syntactic patterns from classic French literary text, and seem particularly promising for the analyses of such particular text.