Minimally supervised event causality identification

Quang Xuan Do; Yee Seng Chan; Dan Roth

Minimally supervised event causality identification

Quang Xuan Do, Yee Seng Chan, Dan Roth

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Pages	294-303
Number of pages	10
State	Published - Oct 3 2011
Event	Conference on Empirical Methods in Natural Language Processing, EMNLP 2011 - Edinburgh, United Kingdom Duration: Jul 27 2011 → Jul 31 2011

Publication series

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

Other

Other	Conference on Empirical Methods in Natural Language Processing, EMNLP 2011
Country	United Kingdom
City	Edinburgh
Period	7/27/11 → 7/31/11

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Cite this

Minimally supervised event causality identification. / Do, Quang Xuan; Chan, Yee Seng; Roth, Dan.

EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. 2011. p. 294-303 (EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Do, QX, Chan, YS & Roth, D 2011, Minimally supervised event causality identification. in EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 294-303, Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, Edinburgh, United Kingdom, 7/27/11.

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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.",

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

T2 - Conference on Empirical Methods in Natural Language Processing, EMNLP 2011

Y2 - 27 July 2011 through 31 July 2011

ER -

Minimally supervised event causality identification

Abstract

Publication series

Other

ASJC Scopus subject areas

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Cite this