基于依存树增强注意力模型的方面级情感分析  

Aspect level sentiment analysis with dependency tree enhanced attention model

作　　者：张文轩 殷雁君 Zhang Wenxuan;Yin Yanjun(College of Computer Science&Technology,Inner Mongolia Normal University,Huhhot 010022,China)

机构地区：[1]内蒙古师范大学计算机科学技术学院,呼和浩特010022

出　　处：《计算机应用研究》2022年第6期1656-1662,共7页Application Research of Computers

基　　金：内蒙古自治区自然科学基金资助项目(2021LHMS06009)。

摘　　要：近年来的方面级情感分析研究尝试利用注意力机制与基于依存树的图卷积模型对上下文词和方面之间的依赖关系进行建模,然而,基于注意力机制的模型具有容易引入噪声信息的缺点,基于依存树的图模型则具有高度依赖于依存树解析质量、鲁棒性较差的缺点。为解决以上问题,探索一种将注意力机制与语法知识相结合的新方法,利用依存树和位置信息分别对注意力机制进行监督,设计并提出了一种用于方面级情感分析的依存树增强的注意力模型,能够更合理地利用语义和句法信息的同时减轻对依存树的依赖程度。在三个基准数据集上进行的实验验证了所提方法的有效性和可解释性。In recent years,the research of aspect level sentiment analysis attempts to model the dependency between context words and aspects by using attention mechanism and graph convolution model based on dependency tree.However,the accuracy of these methods is limited by noise information and lack of differentiated message passing mechanism.In order to solve the above problems,this paper explored a new method combining attention mechanism with syntactic knowledge,used dependency tree and location information to supervise the attention mechanism respectively,and designed and proposed a dependency tree enhanced attention model for aspect level sentiment analysis.The model could make more rational use of semantic and syntactic information and reduce the dependence on dependency tree.Experiments on three benchmark datasets verify the effectiveness and interpretability of the proposed method.

关 键 词：自然语言处理 方面级情感分析 依存树 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]