Detecting salient events in large corpora by a combination of NLP and data mining techniques (poster)

Delphine Battistelli 1 Thierry Charnois 2, 3 Jean-Luc Minel 3 Charles Teissedre 1
1 STIH - Sens, Texte, Informatique, Histoire
2 MoDyCo - Modèles, Dynamiques, Corpus
3 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : In this paper, we present a framework and a system that extracts "salient" events relevant to a query from a large collection of documents, and which also enables events to be placed along a timeline. Each event is represented by a sentence extracted from the collection. We have conducted some experiments showing the interest of the method for this issue. Our method is based on a combination of linguistic modeling (concerning temporal adverbial meanings), symbolic natural language processing techniques (using cascades of morpho-lexical transducers) and data mining techniques (namely, sequential pattern mining under constraints). The system was applied to a corpus of newswires in French provided by the Agence France Presse (AFP). Evaluation was performed in partnership with French newswire agency journalists.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01023926
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Delphine Battistelli, Thierry Charnois, Jean-Luc Minel, Charles Teissedre. Detecting salient events in large corpora by a combination of NLP and data mining techniques (poster). Supplementary Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2013), Mar 2013, Samos, Greece. ⟨hal-01023926⟩

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