Supervised Machine Learning Techniques to Detect TimeML Events in French and English

Béatrice Arnulphy 1 Vincent Claveau 2 Xavier Tannier 3 Anne Vilnat 3
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
3 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
Abstract : Identifying events from texts is an information extraction task necessary for many NLP applications. Through the TimeML specifications and TempEval challenges, it has received some attention in the last years; yet, no reference result is available for French. In this paper, we try to fill this gap by proposing several event extraction systems, combining for instance Conditional Random Fields, language modeling and k-nearest-neighbors. These systems are evaluated on French corpora and compared with state-of-the-art methods on English. The very good results obtained on both languages validate our whole approach.
Keywords : Event identification information extraction TimeML Tem-pEval CRF language modeling English French
Type de document :
Communication dans un congrès
Chris Beimann; Siegfried Handschuch; André Freitas; Farid Meziane; Elisabeth Métais. 20thInternational Conference on Applications of Natural Language to Information Systems, NLDB 2015, Jun 2015, Passau, Germany. Springer, 9103, 2015, Proceedings of the NLDB conference. 〈10.1007/978-3-319-19581-0_2〉
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Béatrice Arnulphy, Vincent Claveau, Xavier Tannier, Anne Vilnat. Supervised Machine Learning Techniques to Detect TimeML Events in French and English. Chris Beimann; Siegfried Handschuch; André Freitas; Farid Meziane; Elisabeth Métais. 20thInternational Conference on Applications of Natural Language to Information Systems, NLDB 2015, Jun 2015, Passau, Germany. Springer, 9103, 2015, Proceedings of the NLDB conference. 〈10.1007/978-3-319-19581-0_2〉. 〈hal-01226541〉

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