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

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.

Domaines: Intelligence artificielle [cs.AI]; Traitement du texte et du document; Apprentissage [cs.LG]

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⟩