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National audience ; Cet article présente une méthode basée sur des calculs de distance et une analyse sémantique et syntaxique pour la segmentation thématique de texte. Pour évaluer cette méthode nous la comparons à un un algorithme lexical très connu : c99. Nous testons les deux méthodes sur un corpus de discours politique français et comparons les résultats. Les deux conclusions qui ressortent de notre expérience sont que les approches sont complémentaires et que les protocoles d'évaluation actuels sont inadaptés.

International audience ; The goal of this paper is to demonstrate that usual evalua- tion methods for text segmentation are not adapted for every task linked to text segmentation. To do so we dierentiated the task of finding text boundaries in a corpus of concatenated texts from the task of finding transitions between topics inside the same text. We worked on a corpus of twenty two French political discourses trying to find boundaries be- tween them when they are concatenated, and to find topic boundaries inside them when they are not. We compared the results of our distance based method to the well known c99 algorithm.

International audience ; The goal of this paper is to demonstrate that usual evalua- tion methods for text segmentation are not adapted for every task linked to text segmentation. To do so we dierentiated the task of finding text boundaries in a corpus of concatenated texts from the task of finding transitions between topics inside the same text. We worked on a corpus of twenty two French political discourses trying to find boundaries be- tween them when they are concatenated, and to find topic boundaries inside them when they are not. We compared the results of our distance based method to the well known c99 algorithm.

International audience ; This paper propose a topical text segmentation method based on intended boundaries detection and compare it to a well known default boundaries detection method, c99. We ran the two methods on a corpus of twenty two French political discourses and results showed us that intended boundaries detection is better than default boundaries detection on well structured text.

International audience ; This paper propose a topical text segmentation method based on intended boundaries detection and compare it to a well known default boundaries detection method, c99. We ran the two methods on a corpus of twenty two French political discourses and results showed us that intended boundaries detection is better than default boundaries detection on well structured text.

International audience ; This paper present a semantic and syntactic distance based method in topic text segmentation and compare it to a very well known text segmentation algorithm: c99. To do so we ran the two algorithms on a corpus of twenty two French political discourses and compared their results.

International audience ; This paper present a semantic and syntactic distance based method in topic text segmentation and compare it to a very well known text segmentation algorithm: c99. To do so we ran the two algorithms on a corpus of twenty two French political discourses and compared their results.

National audience ; Cet article présente une méthode basée sur des calculs de distance et une analyse sémantique et syntaxique pour la segmentation thématique de texte. Pour évaluer cette méthode nous la comparons à un un algorithme lexical très connu : c99. Nous testons les deux méthodes sur un corpus de discours politique français et comparons les résultats. Les deux conclusions qui ressortent de notre expérience sont que les approches sont complémentaires et que les protocoles d'évaluation actuels sont inadaptés.

International audience ; This paper propose a topical text segmentation method based on intended boundaries detection and compare it to a well known default boundaries detection method, c99. We ran the two methods on a corpus of twenty two French political discourses and results showed us that intended boundaries detection is better than default boundaries detection on well structured text.

International audience ; This paper present a semantic and syntactic distance based method in topic text segmentation and compare it to a very well known text segmentation algorithm: c99. To do so we ran the two algorithms on a corpus of twenty two French political discourses and compared their results.

National audience ; Cet article présente une méthode basée sur des calculs de distance et une analyse sémantique et syntaxique pour la segmentation thématique de texte. Pour évaluer cette méthode nous la comparons à un un algorithme lexical très connu : c99. Nous testons les deux méthodes sur un corpus de discours politique français et comparons les résultats. Les deux conclusions qui ressortent de notre expérience sont que les approches sont complémentaires et que les protocoles d'évaluation actuels sont inadaptés.

International audience ; The goal of this paper is to demonstrate that usual evalua- tion methods for text segmentation are not adapted for every task linked to text segmentation. To do so we dierentiated the task of finding text boundaries in a corpus of concatenated texts from the task of finding transitions between topics inside the same text. We worked on a corpus of twenty two French political discourses trying to find boundaries be- tween them when they are concatenated, and to find topic boundaries inside them when they are not. We compared the results of our distance based method to the well known c99 algorithm.

International audience ; The originality of this work leads in tackling text compression using an unsupervised method, based on a deep linguistic analysis, and without resorting on a learning corpus. This work presents a system for dependent tree pruning, while preserving the syntactic coherence and the main informational contents, and led to an operational software, named COLIN. Experiment results show that our compressions get honorable satisfaction levels, with a mean compression ratio of 38 %.

International audience ; The originality of this work leads in tackling text compression using an unsupervised method, based on a deep linguistic analysis, and without resorting on a learning corpus. This work presents a system for dependent tree pruning, while preserving the syntactic coherence and the main informational contents, and led to an operational software, named COLIN. Experiment results show that our compressions get honorable satisfaction levels, with a mean compression ratio of 38 %.

International audience ; The originality of this work leads in tackling text compression using an unsupervised method, based on a deep linguistic analysis, and without resorting on a learning corpus. This work presents a system for dependent tree pruning, while preserving the syntactic coherence and the main informational contents, and led to an operational software, named COLIN. Experiment results show that our compressions get honorable satisfaction levels, with a mean compression ratio of 38 %.