Joint Extraction of Chinese Entity Relations Based on Graph Convolutional Neural Network

Abstract

The existing methods for extracting entity relations usually ignore the complex structural features of Chinese sentences.To address the problem, a Graph Convolutional neural Network(GCN)-based method is proposed for joint extraction of Chinese entity relations.Based on the sequence features extracted by the bidirectional long short term memory network, this method uses GCN to encode the grammatical structure information in dependency analysis results, and employs the idea of an improved entity tagging strategy to build an end-to-end model for the joint extraction of Chinese entity relations.Experimental results show that this method displays an F score of 61.4%, which is 4.1% higher than the LSTM-LSTM model.GCN can effectively encode the prior relations between words contained in the text, and effectively improve the performance of entity relation extraction.