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

Delphine Battistelli; Thierry Charnois; Jean-Luc Minel; Charles Teissèdre

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

Delphine Battistelli ¹ Thierry Charnois ^{2, 3} Jean-Luc Minel ³ Charles Teissèdre ¹

1 STIH - Sens, Texte, Informatique, Histoire

2 Equipe CODAG - Laboratoire GREYC - UMR6072

GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen

3 MoDyCo - Modèles, Dynamiques, Corpus

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 morpholexical 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.

Keywords : Dates Temporal Adverbials Event Extraction Sequential Pattern

Type de document :

Communication dans un congrès

Conference on Intelligent Text Processing and Computational Linguistics, Mar 2013, Samos, Greece. 17 (2), pp.229-237, 2013

Domaine :

Sciences de l'Homme et Société / Sciences de l'information et de la communication

Liste complète des métadonnées

https://halshs.archives-ouvertes.fr/halshs-00921813
Contributeur : Charles Teissèdre <>
Soumis le : samedi 21 décembre 2013 - 17:28:24
Dernière modification le : vendredi 16 novembre 2018 - 02:00:45
Document(s) archivé(s) le : vendredi 21 mars 2014 - 22:45:09

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