用于方面级情感分析的多信息增强图卷积网络

Multi-Information Enhanced Graph Convolutional Network For Aspect Sentiment Analysis

方面级情感分析旨在预测句子中特定方面的情感极性。然而,现阶段的研究依然存在语义信息不充分利用的问题,一方面大多数现有工作侧重于学习上下文词到方面词之间的依存信息,没有充分利用句子的语义信息;另一方面现有研究没有专注于依存树的语法构建,从而没有充分利用语法结构信息去补充语义信息。针对以上问题,提出多信息增强图卷积神经网络(MIE-GCN)模型。主要包括两部分:一是通过方面感知注意力、自注意力和外部常识形成多信息融合层充分利用语义信息;二是根据单词间不同的语法距离构造句子的语法掩码矩阵,通过获得全面语法结构信息来补充语义信息。利用图卷积神经网络增强节点表示。在基准数据集上的实验结果表明,提出的模型均比对比模型有一定的提升。

Aspect level sentiment analysis aims to predict the emotional polarity of specific aspects of a sentence.However,there is still the problem of insufficient use of semantic information in the current stage of research,on the one hand,most of the existing work focuses on learning the dependency information between contextual words and aspect words,and does not make full use of the semantic information of sentences;on the other hand,the existing research does not focus on the syntax construction of dependency trees,so it does not make full use of the grammatical structure information to supplement the semantic information.In view of the above problems,this paper proposes a multi-information augmented graph convolutional neural network(MIE-GCN)model.It mainly includes two parts:one is to form a multi-information fusion layer through aspect perception attention,self-attention and external common sense to make full use of semantic information;the second is to construct a grammatical mask matrix of sentences according to the different grammatical distances between words,and supplement semantic information by obtaining comprehensive grammatical structure information.Finally,the graph convolutional neural network is used to enhance the node representation.The experimental results on the benchmark dataset show that the proposed model has a certain improvement over the comparison model.