首页 | 本学科首页   官方微博 | 高级检索  
     

基于双层CNN-BiGRU-CRF的事件因果关系抽取
引用本文:郑巧夺,吴贞东,邹俊颖. 基于双层CNN-BiGRU-CRF的事件因果关系抽取[J]. 计算机工程, 2021, 47(5): 58-64,72. DOI: 10.19678/j.issn.1000-3428.0057361
作者姓名:郑巧夺  吴贞东  邹俊颖
作者单位:四川师范大学 计算机科学学院, 成都 610101
基金项目:国家自然科学基金青年科学基金项目
摘    要:针对现有事件因果关系抽取方法关系边界识别能力弱和文本语义表征不足的问题,提出一种基于双层CNN-BiGRU-CRF深度学习模型的事件因果关系抽取方法。将因果关系抽取任务转换为两次序列标注任务分别由两层CNN-BiGRU-CRF模型完成,上层模型用于识别事件因果关系语义角色词,其标注结果作为特征输入下层模型划分因果关系边界。在每层模型中,采用突发事件样本数据对BERT模型进行微调,形成文本表示模型以获取语义特征向量矩阵,利用卷积神经网络和双向门控循环单元分别提取局部和全局深层特征,并将上述特征在每个时间序列步进行线性加权融合以增强语义表征能力,最终基于残差思想将高区分度特征输入CRF模型解码完成序列标注任务。在中文突发事件语料集上的实验结果表明,与BiLSTM-Att-规则特征、GAN-BiGRU-CRF等因果关系抽取方法相比,该方法的事件因果关系抽取效果更好,F值达到91.81%,能有效实现事件因果关系的准确抽取。

关 键 词:因果关系抽取  深度学习  卷积神经网络  特征融合  突发事件  
收稿时间:2020-02-10
修稿时间:2020-04-07

Event Causality Extraction Based on Two-Layer CNN-BiGRU-CRF
ZHENG Qiaoduo,WU Zhendong,ZOU Junying. Event Causality Extraction Based on Two-Layer CNN-BiGRU-CRF[J]. Computer Engineering, 2021, 47(5): 58-64,72. DOI: 10.19678/j.issn.1000-3428.0057361
Authors:ZHENG Qiaoduo  WU Zhendong  ZOU Junying
Affiliation:College of Computer Science, Sichuan Normal University, Chengdu 610101, China
Abstract:The existing event causality extraction methods have poor performance in relationship boundary recognition and are limited by insufficient semantic representation of texts.To solve the problem,this paper proposes a method of event causality extraction based on two-layer CNN-BiGRU-CRF deep learning model.The task of causality extraction is transformed into two sequential annotation tasks,which are respectively completed by two-layer CNN-BiGRU-CRF model.The upper model is used to identify the semantic role words of event causality,and the annotation results are input as features into the lower model to divide the causality boundary.In both models,the emergency sample data is used to fine-tune the BERT model to form a text representation model to obtain the semantic feature vector matrix.Convolutional Neural Network(CNN)and Bi-directional Gated Recurrent Unit(BiGRU)are used to extract local and global deep-layer features respectively,and the above features are linearly weighted and fused in each time series step to enhance the semantic representation.Finally,based on the idea of residuals,high resolution features are input into the CRF model for decoding and to complete the task of sequence annotation.Experimental results on Chinese emergency corpus show that compared with the causality extraction methods such as BiLSTM-Att-rule feature and GAN-BiGRU-CRF,the proposed method has better causality extraction performance with F value reaching 91.81%.It can effectively achieve the accurate extraction of event causality.
Keywords:causality extraction  deep learning  Convolutional Neural Network(CNN)  feature integration  emergency event
本文献已被 维普 万方数据 等数据库收录!
点击此处可从《计算机工程》浏览原始摘要信息
点击此处可从《计算机工程》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号