Minimally Supervised Event Causality Identification

Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 294–303, Edinburgh, Scotland, UK, July 27–31, 2011. c⃝2011 Association for Computational Linguistics Minimally Supervised Event Causality Identification Quang Xuan Do Yee Seng Chan Department of Computer ScienceDan Roth University of Illinois at Urbana-Champaign Urbana, IL 61801, USA {quangdo2,chanys,danr}@illinois.edu Abstract 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. 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 re- lations between events is important for various nat- ural language processing (NLP) applications such as question answering, etc. In this work, we auto- matically detect and extract causal relations between events in text. Despite its importance, prior work on event causality extraction in context in the NLP litera- ture is relatively sparse. In (Girju, 2003), the au- thor used noun-verb-noun lexico-syntactic patterns to learn that “mosquitoes cause malaria”, where the cause and effect mentions are nominals and not nec- essarily event evoking words. In (Sun et al., 2007), the authors focused on detecting causality between search query pairs in temporal query logs. (Beamer and Girju, 2009) tried to detect causal relations be- tween verbs in a corpus of screen plays, but limited themselves to consecutive, or adjacent verb pairs. In (Riaz and Girju, 2010), the authors first cluster sentences into topic-specific scenarios, and then fo- cus on building a dataset of causal text spans, where each span is headed by a verb. Thus, their focus was not on identifying causal relations between events in a given text document. In this paper, given a text document, we first iden- tify events and their associated arguments. We then identify causality or relatedness relations between event pairs. To do this, we develop a minimally su- pervised approach using focused distributional sim- ilarity methods, such as co-occurrence counts of events collected automatically from an unannotated corpus, to measure and predict existence of causal- ity relations between event pairs. Then, we build on the observation that discourse connectives and the particular discourse relation they evoke in context provide additional information towards determining causality between events. For instance, in the …

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