基于提示增强与双图注意力网络的复杂因果关系抽取  

Complex causal relationship extraction based on prompt enhancement and bi-graph attention network

作　　者：邓金科 段文杰 张顺香[1,2] 汪雨晴 李书羽 李嘉伟 DENG Jinke;DUAN Wenjie;ZHANG Shunxiang;WANG Yuqing;LI Shuyu;LI Jiawei(School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan Anhui 232001,China;Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei Anhui 230088,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001 [2]合肥综合性国家科学中心人工智能研究院,合肥230088

出　　处：《计算机应用》2024年第10期3081-3089,共9页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62076006);安徽高校协同创新项目(GXXT-2021-008)。

摘　　要：针对复杂因果句实体密度高、句式冗长等特点导致的外部信息不足和信息传递遗忘问题,提出一种基于提示增强与双图注意力网络(BiGAT)的复杂因果关系抽取模型PE-BiGAT(PromptEnhancementandBi-Graph Attention Network)。首先,抽取句子中的结果实体并与提示学习模板组成提示信息,再通过外部知识库增强提示信息;其次,将提示信息输入BiGAT,同时结合关注层与句法和语义依存图,并利用双仿射注意力机制缓解特征重叠的情况,增强模型对关系特征的感知能力;最后,用分类器迭代预测句子中的所有因果实体,并通过评分函数分析句子中所有的因果对。在SemEval-2010 task 8和AltLex数据集上的实验结果表明,与RPA-GCN(Relationship Position and Attention-Graph Convolutional Network)相比,所提模型的F1值提高了1.65个百分点,其中在链式因果和多因果句中分别提高了2.16和4.77个百分点,验证了所提模型在处理复杂因果句时更具优势。A complex causal relationship extraction model based on prompt enhancement and Bi-Graph ATtention network(BiGAT)—PE-BiGAT(Prompt Enhancement and Bi-Graph Attention Network)was proposed to address the issues of insufficient external information and information transmission forgetting caused by the high density and long sentence patterns of complex causal sentences.Firstly,the result entities from the sentence were extracted and combined with the prompt learning template to form the prompt information,and the prompt information was enhanced through an external knowledge base.Then,the prompt information was input into the BiGAT,the attention layer was combined with syntax and semantic dependency graphs,and the biaffine attention mechanism was used to alleviate feature overlapping and enhance the model’s perception of relational features.Finally,all causal entities in the sentence were predicted iteratively by the classifier,and all causal pairs in the sentence were analyzed through a scoring function.Experimental results on SemEval-2010 task 8 and AltLex datasets show that compared with RPA-GCN(Relationship Position and Attention‑Graph Convolutional Network),the proposed model improves the F1 score by 1.65 percentage points,with 2.16 and 4.77 percentage points improvements in chain causal and multi-causal sentences,which confirming that the proposed model has an advantage in dealing with complex causal sentences.

关 键 词：复杂因果关系抽取 提示增强 双图注意力网络 双仿射注意力 评分函数 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]