BGCN:Trigger Detection Based on BERT and Graph Convolution Network

• Published in: Ji suan ji ke xue Vol. 48; no. 7; p. 292
• Main Authors: Cheng, Si-Wei, Ge, Wei-Yi, Wang, Yu, Xu, Jian
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01-01-2021
• Subjects: Convolution; Feature extraction; Neural networks; Representations; Sentences
• ISSN: 1002-137X

Trigger word detection is a basic task of event extraction, which involves the recognition and classification of trigger words.There are two main problems in the previous work:(1)the neural network model for trigger word detection only consi-ders the sequential representation of sentences, and the sequential modeling method is inefficient in capturing long-distance dependencies;(2)although the representation-based method overcomes the problem of manual feature extraction, the word vector used as the initial training feature lacks the degree of representation of the sentence, so it is difficult to capture the deep two-way representation.Therefore, we propose a trigger word detection model BGCN,based on BERT model and GCN network.This model strengthens the feature representation by introducing BERT word vector, and introduces syntactic structure to capture long-distance dependencies and detect event trigger words.Experimental results show that our method outperforms other existing neural network models on ACE20