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The Role of Textual Graph Patterns in Discovering Event Causality

The Role of Textual Graph Patterns in Discovering Event Causality

Bryan Rink, Cosmin Adrian Bejan, Sanda Harabagiu
Copyright: © 2012 |Pages: 17
ISBN13: 9781609607418|ISBN10: 1609607414|EISBN13: 9781609607425
DOI: 10.4018/978-1-60960-741-8.ch019
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MLA

Rink, Bryan, et al. "The Role of Textual Graph Patterns in Discovering Event Causality." Applied Natural Language Processing: Identification, Investigation and Resolution, edited by Philip M. McCarthy and Chutima Boonthum-Denecke, IGI Global, 2012, pp. 334-350. https://doi.org/10.4018/978-1-60960-741-8.ch019

APA

Rink, B., Bejan, C. A., & Harabagiu, S. (2012). The Role of Textual Graph Patterns in Discovering Event Causality. In P. McCarthy & C. Boonthum-Denecke (Eds.), Applied Natural Language Processing: Identification, Investigation and Resolution (pp. 334-350). IGI Global. https://doi.org/10.4018/978-1-60960-741-8.ch019

Chicago

Rink, Bryan, Cosmin Adrian Bejan, and Sanda Harabagiu. "The Role of Textual Graph Patterns in Discovering Event Causality." In Applied Natural Language Processing: Identification, Investigation and Resolution, edited by Philip M. McCarthy and Chutima Boonthum-Denecke, 334-350. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-741-8.ch019

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Abstract

We present a novel method for discovering causal relations between events encoded in text. In order to determine if two events from the same sentence are in a causal relation or not, we first build a graph representation of the sentence that encodes lexical, syntactic, and semantic information. From such graph representations we automatically extract multiple graph patterns (or subgraphs). The patterns are sorted according to their contribution to the expression of intra-sentential causality between events. To decide whether a pair of events is in a causal relation, we employ a binary classifier that uses the graph patterns. Our experimental results indicate that capturing causal event relations using graph patterns outperforms existing methods.

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