Chinese Event Detection Without Triggers Based on Dual Attention

Abstract

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 converted into a binary classification task for solving multi-label problem.Finally,the focal loss function is used to address the sample imbalance after conversion.Experimental results on the ACE2005 Chinese corpus show that compared with the best baseline model JMCEE,the accuracy rate,recall rate,and F1-score of the proposed model increases by 3.40%,3.90% and 3.67%,respectively.

Keywords

• double attention|without triggers|chinese event detection|ace2005|binary classification