Finding salient dates for building thematic timelines

Authors:
Remy Kessler;Xavier Tannier;Caroline Hagège;Véronique Moriceau;André Bittar
Affiliations:
LIMSI-CNRS, Orsay, France;Univ. Paris-Sud, LIMSI-CNRS, Orsay, France;Xerox Research Center Europe, Meylan, France;Univ. Paris-Sud, LIMSI-CNRS, Orsay, France;Xerox Research Center Europe, Meylan, France
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Year:
2012

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Abstract

We present an approach for detecting salient (important) dates in texts in order to automatically build event timelines from a search query (e.g. the name of an event or person, etc.). This work was carried out on a corpus of newswire texts in English provided by the Agence France Presse (AFP). In order to extract salient dates that warrant inclusion in an event timeline, we first recognize and normalize temporal expressions in texts and then use a machine-learning approach to extract salient dates that relate to a particular topic. We focused only on extracting the dates and not the events to which they are related.