Employing Gated Mechanism to Incorporate Multi-features into Chinese Event Coreference Resolution

Abstract

Event coreference resolution is the basis of many natural language processing tasks,aiming to identify event mentions in text that refer to the same real event.Since Chinese grammar is much more complex than English,the method of capturing English text features is not effective in Chinese event corefe-rence resolution.To solve the within-document Chinese event corefe-rence,a gated mechanism neural network(GMNN) is proposed.In view of Chinese characteristics with subject omission and loose structure,event attributes are introduced as symbolic features.On this basis,a novel gated mechanism is proposed,which fine-tunes the symbolic feature vector,filters the noise in the symbolic features,extracts useful information in a specific context,and improves the coreference events recognition rate.Experimental results on the ACE2005 Chinese dataset show that the perfor-mance of GMNN improves by 2.66,which effectively improves the effect of Chinese event coreference resolution.

Keywords

• chinese event coreference resolution|gated mechanism|neural network|pre-trained language models|symbolic features