Identifying Strategic Information from Scientific Articles through Sentence Classification.

Abstract : We address here the need to assist users in rapidly accessing the most important or strategic information in the text corpus by identifying sentences carrying specific information. More precisely, we want to identify contribution of authors of scientific papers through a categorization of sentences using rhetorical and lexical cues. We built local grammars to annotate sentences in the corpus according to their rhetorical status: objective, new things, results, findings, hypotheses, conclusion, related_word, future work. The annotation is automatically projected automatically onto two other corpora to test their portability across several domains. The local grammars are implemented in the Unitex system. After sentence categorization, the annotated sentences are clustered and users can navigate the result by accessing specific information types. The results can be used for advanced information retrieval purposes.
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Communication dans un congrès
6th International Conference on Language Resources and Evaluation Conference (LREC-08), May 2008, Marrakesh, Morocco. ELDA, pp.1518-1522, 2008


https://hal.archives-ouvertes.fr/hal-00635663
Contributeur : Fidelia Ibekwe-Sanjuan <>
Soumis le : mardi 25 octobre 2011 - 16:45:41
Dernière modification le : mercredi 23 mars 2016 - 09:48:48
Document(s) archivé(s) le : jeudi 15 novembre 2012 - 10:31:48

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Fidelia Ibekwe-Sanjuan, Chaomei Chen, Pinho Roberto. Identifying Strategic Information from Scientific Articles through Sentence Classification.. 6th International Conference on Language Resources and Evaluation Conference (LREC-08), May 2008, Marrakesh, Morocco. ELDA, pp.1518-1522, 2008. <hal-00635663>

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