Enhancing Chinese Event Extraction with Event Trigger Structures

The dependency syntactic structure is widely used in event extraction. However, the dependency structure reflecting syntactic features is essentially different from the event structure that reflects semantic features, leading to the performance degradation. In this paper, we propose to use Event Trigger Structure for Event Extraction (ETSEE), which can compensate the inconsistency between two structures. First, we leverage the ACE2005 dataset as case study, and annotate 3 kinds of ETSs, i.e., “light verb + trigger”, “preposition structures” and “tense + trigger”. Then we design a graph-based event extraction model that jointly identifies triggers and arguments, where the graph consists of both the dependency structure and ETSs. Experiments show that our model significantly outperforms the state-of-the-art methods. Through empirical analysis and manual observation, we find that the ETSs can bring the following benefits: (1) enriching trigger identification features by introducing structural event information; (2) enriching dependency structures with event semantic information; (3) enhancing the interactions between triggers and candidate arguments by shortening their distances in the dependency graph.