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The Role of Textual Graph Patterns in Discovering Event Causality
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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