Treasures Outside Contexts: Improving Event Detection via Global Statistics

Abstract

Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying on the contextual semantic features of each word in the input text, which we find is easy to get confused by varied contexts in the test stage. To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with the contextual features. Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (S2-JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. In experiments, S2-JDN effectively exceeds ten recent state-of-the-art (SOTA) baseline methods on ACE2005 and KBP2015 benchmark datasets. Further, we perform extensive experiments to investigate the effectiveness of S2-JDN.