Paper: Minimally Supervised Event Causality Identification

ACL ID D11-1027
Title Minimally Supervised Event Causality Identification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

This paper develops a minimally supervised approach, based on focused distributional sim- ilarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identify- ing causality, we observe that discourse con- nectives and the particular discourse relation they evoke in context provide additional in- formation towards determining causality be- tween events. We show that combining dis- course relation predictions and distributional similarity methods in a global inference pro- cedure provides additional improvements to- wards determining event causality.