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

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.
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https://halshs.archives-ouvertes.fr/halshs-00921813
Contributor : Charles Teissèdre <>
Submitted on : Saturday, December 21, 2013 - 5:28:24 PM
Last modification on : Tuesday, November 19, 2019 - 9:45:14 AM
Long-term archiving on: Friday, March 21, 2014 - 10:45:09 PM

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  • HAL Id : halshs-00921813, version 1

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Delphine Battistelli, Thierry Charnois, Jean-Luc Minel, Charles Teissèdre. Detecting salient events in large corpora by a combination of NLP and data mining techniques. Conference on Intelligent Text Processing and Computational Linguistics, Mar 2013, Samos, Greece. pp.229-237. ⟨halshs-00921813⟩

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