Dynamic Structure Hypergraph for Document-level Event Extraction

Abstract

Document-level Event Extraction (DEE) aims to identify event information from a given document. The two challenges of this task are the event arguments scattering across differrent sentences and the multiple events within a single document. In this paper, we propose a novel Dynamic Structure Hypergraph model to address the issue of limited global modeling capability in traditional graphs. Firstly, we construct a hypergraph to model the global interactions between different sentences and entities in a document. Then, new hyperedges are generated by constructing a mention-mention correlation matrix based on the updated node representations, which evolves the hypergraph into a dynamic structure. This will help the nodes to aware the contextual semantic information in time. Finally, extensive experiments and analysis demonstrate that our method has made significant improvements in addressing the two aforementioned challenges, which outperforms existing state-of-the-art models on two public datasets. Our code is available at https://github.com/1999rq/DSH.