Using a bigram event model to predict causal relations

Brandon Beamer, Roxana Girju

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

Abstract

This paper addresses the problem of causal knowledge discovery. Using online screenplays, we generate a corpus of temporally ordered events. We then introduce a measure we call causal potential which is easily calculated with statistics gathered over the corpus and show that this measure is highly correlated with an event pair's tendency of encoding a causal relation. We suggest that causal potential can be used in systems whose task is to determine the existence of causality between temporally adjacent events, when critical context is either missing or unreliable. Moreover, we argue that our model should therefore be used as a baseline for standard supervised models which take into account contextual information.

Original languageEnglish (US)
Title of host publication10th International Conference on Intelligent Text Processing and Computational Linguistics
PublisherCICLING
Pages430-441
Number of pages12
ISBN (Print)3642003818, 9783642003813
DOIs
StatePublished - Jul 21 2009
Event10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009 - Mexico City, Mexico
Duration: Mar 1 2009Mar 7 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009
CountryMexico
CityMexico City
Period3/1/093/7/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Beamer, B., & Girju, R. (2009). Using a bigram event model to predict causal relations. In 10th International Conference on Intelligent Text Processing and Computational Linguistics (pp. 430-441). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5449 LNCS). CICLING. https://doi.org/10.1007/978-3-642-00382-0_35