MASGC：融合特定屏蔽机制的简单图卷积情感分析模型

方面级情感分析目前是基于图卷积神经网络(GCN)来整合句子的语法结构，它能够有效地解决长范围词汇依赖不准确的问题，但GCN却拥有不必要的复杂性和冗余计算。此外，它忽略了属性与上下文之间相对位置的关系。为此，提出了一种新的模型来解决上述问题。首先建立双向GRU层，接着使用位置感知转换增加靠近方面词的上下文词的重要程度，然后通过移除非线性和折叠连续层之间的权重矩阵来降低复杂性；再与特定屏蔽层进行融合实现单层MASGC结构，生成一种新的基于检索上下文的注意力机制；最后通过全连接层给出分类结果。该模型在五个数据集上进行了大量实验，实验结果表明其具有更高的准确率和更少的训练时间。

MASGC:simple graph convolutional emotion analysis model with specific masking

At present, aspect-based sentiment analysis based on graph convolution neural network(GCN) integrates the grammatical structure of sentences. It can effectively solve the problem of inaccurate long-range lexical dependence. However, GCN has unnecessary complexity and redundant computing. In addition, it ignores the relative position relationship between attributes and context. This paper proposed a new model to solve these problems. Firstly, the model established a bidirectional GRU layer. Secondly, it used position-aware transformation to increase the importance of context words close to aspect words, and then reduced the complexity by removing nonlinearity and folding the weight matrix between successive layers. Thirdly, it fused the specific mask layer to realize the single-layer MASGC(masked simple graph convolutional networks) structure, and then generated a new attention mechanism based on retrieval context. Finally, it gave the classification results through the full connection layer. The model carries out a large number of experiments on five data sets. The experimental results show that it has higher accuracy and less training time.