融合SPO语义和句法信息的事件检测方法  

Detecting Events with SPO Semantic and Syntactic Information

作　　者：何丽[1] 杨美华 刘璐瑶 He Li;Yang Meihua;Liu Luyao(College of Science and Technology,Tianjin University of Finance and Economics,Tianjin 300222,China)

机构地区：[1]天津财经大学理工学院,天津300222

出　　处：《数据分析与知识发现》2023年第9期114-124,共11页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金青年项目(项目编号:19CGL025)的研究成果之一。

摘　　要：【目的】利用SPO三元组语义信息和依存句法关系类型信息提升事件检测模型的性能。【方法】融合SPO三元组语义信息和依存句法关系类型信息构造事件检测模型EDMC3S。该模型以语句的依存句法树为基础生成SPO三元组和依存句法关系类型矩阵,使用多头注意力机制对SPO三元组进行语义特征强化,利用自注意力机制对不同的依存关系类型进行权重分配后,通过多阶图注意力聚合网络对语句的全局句法和语义特征进行提取,最后使用一个全连接层对SPO三元组语义特征和语句全局特征进行整合。【结果】在ACE2005数据集上的实验结果显示,EDMC3S事件检测模型在触发词识别与事件类型分类这两个子任务中获得了较好的分类性能。在P、R和F1值三个评价指标上触发词识别分别达到80.6%、82.4%和81.5%,事件类型分类分别达到78.7%、80.1%和79.4%。【局限】仅在ACE2005数据集上进行实验验证。【结论】SPO三元组语义特征和词之间依存句法关系类型的引入能够提升事件检测中的触发词识别和事件类型分类效果。[Objective]This paper utilizes the SPO triples and the dependency syntax to improve the performance of the event detection model.[Methods]We constructed an event detection model EDMC3S combining the semantic information of SPO triples and the type information of dependency syntactic relationship.First,the model generates SPO triples and dependency syntax relation type weight matrix based on the dependency syntax tree of the sentence.Then,we used a multi-head attention mechanism to strengthen the semantic features of SPO triples and a self-attention mechanism to distribute the weight of different dependency relation types.Third,we extracted the global syntactic and semantic features through the multi-order graph attention aggregation network.Finally,we integrated the semantic features of SPO triples and the global features of statements with a connection layer.[Results]We examined the new model on the ACE2005 dataset,and it achieved better classification performance in the two sub-tasks of trigger word recognition and event type classification.On the three evaluation indexes of P,R,and F1,the recognition of trigger words reached 80.6%,82.4%,and 81.5%,respectively,and the classification of event types reached 78.7%,80.1%,and 79.4%,respectively.[Limitations]We need to evaluate the new model with more datasets.[Conclusions]The proposed model can improve the effect of event detection in trigger word recognition and event type classification.

关 键 词：事件检测 SPO语义信息 句法信息 注意力机制 多阶图注意力聚合网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] G350[自动化与计算机技术—计算机科学与技术]