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In this paper we introduce a machine learning approach for automatic text segmentation. Our text segmenter clusters text-segments containing similar concepts. It first discovers the different concepts present in a text, each concept being defined as a set of representative terms. After that the text is partitioned into coherent paragraphs using a hard clustering technique based on the Classification Maximum Likelihood approach. We evaluate the effectiveness of this technique on a set of concatenated paragraphs from the %'& ( ) +-, . & data collection and compare it to a well-established text segmentation technique proposed by Salton et al. MOTS-CLÉS : Segmentation de texte, Apprentissage non-supervisé, Partition de mots, Vraisemblance classifiante.

Apprentissage non-supervisé pour la segmentation automatique de textes