Event Detection with Cross-Sentence Graph Convolutional Networks

Abstract

The goal of Event Detection (ED) task is to identify the words that mark the occurrence of events in text, and classify them into a set of event types. To model informative word semantics, some researchers apply Graph Convolutional Network (GCN) to exploit the syntactic graph transformed from the dependency tree, within one sentence. We are motivated to simultaneously leverage syntactic clues and context information across sentences. To this end, we propose a novel ED model with Cross-Sentence Graph Convolutional Networks (CSGCN). The CSGCN contains two main components, including a tree extension module and the syntax-aware graph convolution. Each sentence is parsed to a dependency tree by an automatic toolkit. The first module merges entity-specific subtrees from neighbor sentences into the dependency tree of current sentence, which constructs a cross-sentence dependency tree. On this basis, we transform the tree into an undirected graph. After that, a syntax-aware attention mechanism is employed in the computation of graph convolution. This mechanism dynamically captures syntax-relevant information from neighbor nodes via the graph structure. Finally, we devise an entity aggregation module to aggregate key entity information for trigger candidates. We conduct experiments on the ACE 2005 and KBP 2017 datasets. The results show that our model achieves satisfactory and competitive performance on ACE 2005, and outperforms all State-of-The-Art models on KBP 2017.