基于渐进增强与图卷积的方面级情感分析模型

方面级情感分析的任务目标是对评论中的特定方面词情感极性的判别，近年来的大多研究方法都采用句法依存树结合图卷积网络来构建模型，但是对句法依存结构的使用过于直接且忽略了在生成树是伴随的噪声影响，因此提出了一种渐进增强结合双向图卷积模块的情感分类模型（PCB-GCN）。首先，设计渐进增强算法来获取更加特异性的句法依存树，利用BiLSTM来提取语义，同时针对不同方向的句法图结构采用双向图卷积模块进行特征提取，最后将句法特征与上下文语义通过协同融合网络结合起来进行最终分类。模型在多组公开数据集上进行了实验，均取得了相比目前基线模型更好的效果。

Aspect based sentiment analysis with progressive enhancement and graph convolution

The purpose of aspect-level sentiment analysis is to determine the sentiment of specific aspect words in a sentence. In recent years, many methods have adopted syntactic dependency tree combined with graph convolutional network modeling. But the use of syntactic dependency structures is too direct and ignores the noise effect that accompanies the spanning tree, which limits the use of syntactic relations. This papaer proposed an emotional classification model(PCB-GCN) with progressive enhancement combined with a bidirectional graph convolution module. Firstly, designed a progressive enhancement algorithm to obtain richer syntactic relations, used Bi-LSTM to extract semantics, and used bidirectional graph convolution module for feature extraction for syntactic graph structures in different directions. Finally, it combined the syntactic features and context semantics through a collaborative network, combined them for the final classification. The model has been tested on multiple public data sets, and all have achieved better results than the current baseline model.