基于图卷积神经网络的中文实体关系联合抽取

现有实体关系联合抽取方法未充分考虑中文句子中实体关系的复杂结构特征，为此，提出一种基于图卷积神经网络（GCN）的中文实体关系联合抽取方法。在双向长短时记忆网络抽取序列特征的基础上，利用GCN编码依存分析结果中的语法结构信息，借鉴改进的实体标注策略构建端到端的中文实体关系联合抽取模型。实验结果表明，该方法的F值可达61.4%，相比LSTM-LSTM模型提高了4.1%，GCN能有效编码文本的先验词间关系并提升实体关系抽取性能。

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

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.