Predicting unknown time arguments based on cross-event propagation

Prashant Gupta, Heng Ji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many events in news articles don't include time arguments. This paper describes two methods, one based on rules and the other based on statistical learning, to predict the unknown time argument for an event by the propagation from its related events. The results are promising - the rule based approach was able to correctly predict 74% of the unknown event time arguments with 70% precision.

Original languageEnglish (US)
Title of host publicationACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.
PublisherAssociation for Computational Linguistics (ACL)
Pages369-372
Number of pages4
ISBN (Print)9781617382581
DOIs
StatePublished - 2009
Externally publishedYes
EventJoint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009 - Suntec, Singapore
Duration: Aug 2 2009Aug 7 2009

Publication series

NameACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.

Other

OtherJoint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009
CountrySingapore
CitySuntec
Period8/2/098/7/09

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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