Chinese Event Detection Without Triggers Based on Dual Attention

Event extraction is an essential task of natural language processing, and event detection is one of the critical steps of event extraction, whose goal is to detect the occurrence of events and classify them.Currently, Chinese event detection has problems of polysemous words and mismatches between words and triggers, which affect the accuracy of event detection models.We propose the event detection without triggers based on dual attention（EDWTDA）,which skips the process of trigger word recognition and directly determines event types without trigger word tags.First, the ALBERT model is applied to improve the semantic representation ability of word embedding vectors.Second, we fusion local attention and event types to capture key semantic information and simulate hidden event triggers to solve the problem of mismatch between words and triggers.Third, the global attention is introduced to mine contextual information in documents to solve the problem of polysemous words.Further, the event detection task is convert