结合句法依存信息的方面级情感分类

引入句法依存信息到原方面术语,提出一种新的方面术语表示方法,利用Glove词向量表示单词以及单词与单词之间的依存关系,构造出包含句法依存信息的依存关系邻接矩阵和依存关系表示矩阵,利用图卷积神经网络和多头注意力机制将句法依存信息融入到方面术语中,使得方面术语表达与上下文结构高度相关。将改进后的方面词术语表示替换到现有模型后,模型泛化能力得到有效提升。对比试验和分析结果表明:该方法具有有效性和泛化性。

Aspect-level sentiment classification combined with syntactic dependency information

Considering introducing syntactic dependency information into the original aspect terms, a new aspect term representation method was proposed. First Glove word vector was used to represent the words and dependency relationship between words, and the dependency adjacency matrix and the representation of dependency relationship matrix including syntactic dependency information was constructed. Then graph convolution neural network and multi-head attention mechanism were used to integrate syntactic dependency information into aspect terms, so that aspect terms were highly related to context structure. The models generalization ability were effectively improved by replacing the existing models with the improved aspect term expression. Through comparative experiments and analysis, effectiveness and generalization of the method were proved.