ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination

As an important task in the field of information extraction, event detection is widely used in event graph construction and network public opinion monitoring. Although the existing methods (such as BGCN, MGRN-EE, etc.) have obtained well performance on event detection by utilizing various features from text, they neglect that the events in data follows a long-tailed distribution, which leads to a serious bias in the trained event detection model. By following a simple but effective way to address this issue, we propose an event detection model based on event sentence pre-determination, termed as ES4ED. The model first employs classification method to identify the sentences that contain events semantically (called event sentences), and then conducts event detection on these event sentences to solve the long-tailed distribution of events. ES4ED consists of three components: the semantic encoder, the event sentence decider and the event detector. First, the semantic encoder encodes the words semantically. Then, the event sentence decider identifies event sentences by classification. Finally, the event sentences are input to the event detector to complete the event triggers identification and classification. Experimental results on the public dataset ACE2005 show that the F1 score of the proposed model achieves 79.2% and 76.5% on trigger identification and trigger classification, respectively, which are significantly improved compared with the existing typical works.