Inferring Sentence-internal Temporal Relations

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

Abstract

In this paper we propose a data intensive approach for inferring sentence-internal temporal relations, which relies on a simple probabilistic model and assumes no manual coding.
We explore various combinations of features, and evaluate performance against a gold-standard corpus and human subjects performing the same task. The best model achieves
70.7% accuracy in inferring the temporal relation between two clauses and 97.4% accuracy in ordering them, assuming that the temporal relation is known.
Original languageEnglish
Title of host publicationProceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004
PublisherAssociation for Computational Linguistics
Pages153-160
Number of pages8
Publication statusPublished - 2004
EventHLT-NAACL 2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics - Boston, MA, United States
Duration: 2 May 20047 May 2004

Conference

ConferenceHLT-NAACL 2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics
CountryUnited States
CityBoston, MA
Period2/05/047/05/04

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