Improving document-level event detection with event relation graph

The correlation between events within the same document plays a crucial role in event detection, most existing detection models often ignore event correlations, which is not applicable to multi-event detection at the document level. In the real world, it is a common phenomenon that the probability of correlated events occurring simultaneously is much greater than the probability of uncorrelated events occurring simultaneously. Based on this observation, we propose an event correlation-based document-level event detection model (EventCo-ED) to capture the document-level association between events. Specifically, EventCo-ED first construct a novel event relation graph (ERG) to capture the correlation between events, and use this correlation to extract the topic features of a document. Secondly, DMBERT is employed to get sentence-level contextual representation as the local features. Finally, a gated feature fusion module is used to aggregate topic features and local features, and a correlation suppression module is used to increase the probability that related events are detected simultaneously and suppress the probability that unrelated events are detected simultaneously. Experimental results show that the proposed model can simultaneously improve the precision and recall of multi-event detection, and achieve 1.56% F1 and 3.63% F1 improvement in LEVEN corpus and MAVEN corpus