【摘要】 针对目前方面级情感分析存在句法依赖解析结果不准确、句法和语义信息没有充分利用的问题，提出一种双特征增强的图卷积网络。利用句法解析器中的依赖概率矩阵作为图卷积网络的邻接矩阵，减小解析结果的不准确性，对初始句法信息进行上下文动态加权增强提取句法信息的能力，对于语义信息，采用多头注意力机制构建动态语义图卷积网络，充分利用语义空间信息。实验结果表明，与基线模型相比模型取得了较明显的性能提升。更多还原

【Abstract】 A dual feature enhanced graph convolutional network was proposed to address the current issue of inaccurate syntactic dependency parsing results and insufficient utilization of syntactic and semantic information in aspect level sentiment analysis. The dependency probability matrix in the syntactic parser was utilized as the adjacency matrix of the graph convolutional network, reducing the inaccuracy of analysis results. The initial syntactic information was dynamically weighted to enhance the ability in extracting syntactic information. For semantic information, a multi head attention mechanism was used to construct a dynamic semantic graph convolutional network, fully utilizing semantic spatial information. Experimental results show that compared with the baseline model, the model achieves performance improvement.更多还原