tailieunhanh - Minimally Supervised Event Causality Identification

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 | EMNLP 11 Minimally Supervised Event Causality Identification Quang Xuan Do Yee Seng Chan Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL 61801 USA quangdo2 chanys danr @ 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. 1 Introduction An important part of text understanding arises from understanding the semantics of events described in the narrative such as identifying the events that are mentioned and how they are related semantically. For instance when given a sentence The police arrested him because he killed someone. humans understand that there are two events triggered by the words arrested and killed and that there is a causality relationship between these two events. Besides being an important component of discourse understanding automatically identifying causal relations between events is important for various natural language processing NLP applications such as question answering etc. In this work we automatically detect and extract causal relations between events in text. Despite its importance prior work on event causality extraction in context in the NLP literature is relatively sparse. In Girju 2003 the author used noun-verb-noun lexico-syntactic patterns to learn that mosquitoes cause malaria where the cause and effect mentions are nominals and not necessarily event evoking words. In Sun et al. .

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