Minimally supervised event causality identification

Quang Xuan Do, Yee Seng Chan, Dan Roth

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

Abstract

This paper develops a minimally supervised approach, based on focused distributional similarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identifying causality, we observe that discourse connectives and the particular discourse relation they evoke in context provide additional information towards determining causality between events. We show that combining discourse relation predictions and distributional similarity methods in a global inference procedure provides additional improvements towards determining event causality.

Original languageEnglish (US)
Title of host publicationEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Pages294-303
Number of pages10
StatePublished - Oct 3 2011
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2011 - Edinburgh, United Kingdom
Duration: Jul 27 2011Jul 31 2011

Publication series

NameEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Other

OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2011
CountryUnited Kingdom
CityEdinburgh
Period7/27/117/31/11

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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