Event detection with multi-order edge-aware graph convolution networks

Abstract

Event detection, specifically, identifying the type of events in a piece of text, is a crucial task in information extraction. Previous event detection task research proved that a syntactic graph based on a dependency tree can be integrated into a graph convolutional neural network to better capture the context of a sentence. However, most of the existing studies rely only on first-order syntactic relations and usually ignore dependence label information. In this paper, we propose a multi-order edge-aware graph convolution network (MEA-GCN) based on multi-order grammar and typed dependent label information. We use the BERT representation and the multi-head attention update mechanism to generate a variety of multi-order syntactic graph representations; consequently, our model can automatically learn dependent information and improve the representation ability of the syntactic relations. The experimental results on the widely used ACE 2005 and TAC KBP 2015 show that our model achieves significant improvement over the competitive baseline methods.