Abstract
Existing methods that use document-level information for event detection ignore the dependencies between sentences and also have shortcomings in modeling the dependencies among words. In this paper, we propose a novel Hierarchical Graph Enhanced Event Detection (HGEED) framework to make full use of syntax and document information for the task of event detection. First, a sentence graph is used to model word-to-word dependencies, enriching the local information of words by incorporating syntactic features. Then, a document graph is built to model sentence-to-sentence dependencies, obtaining global semantic representations for word-level prediction. The experiment results on the widely used ACE 2005 and TAC KBP 2015 corpora show that our model can capture local and global information with dependencies and achieve significant improvements as compared to all baselines.
Published Version
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