Event Detection with Relation-Aware Graph Convolutional Neural Networks
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Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific types of events in text. Recently, graph convolutional networks (GCNs) over dependency trees have been widely used to capture syntactic structure information and get convincing performances in event detection. However, these works ignore the syntactic relation labels on the tree, which convey rich and useful linguistic knowledge for event detection. In this paper, we investigate a novel architecture named Relation-Aware GCN (RA-GCN), which efficiently exploits syntactic relation labels and models the relation between words specifically. We first propose a relation-aware aggregation module to produce expressive word representation by aggregating syntactically connected words through specific relation. Furthermore, a context-aware relation update module is designed to explicitly update the relation representation between words, and these two modules work in the mutual promotion way. Experimental results on the ACE2005 dataset show that our model achieves a new state-of-the-art performance for event detection.
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