论文标题
基于方面情感分析的关系图表网络
Relational Graph Attention Network for Aspect-based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析旨在确定在线评论中特定方面的情感极性。最近的大多数努力采用了基于注意力的神经网络模型，将各个方面与意见单词联系起来。但是，由于语言的复杂性和单个句子中多个方面的存在，这些模型通常会使连接感到困惑。在本文中，我们通过有效编码语法信息来解决此问题。首先，我们通过重塑和修剪普通的依赖性解析树来定义统一面向方面的依赖树结构，该结构扎根于目标方面。然后，我们提出了一个关系图注意网络（R-GAT），以编码新的树结构以进行情感预测。广泛的实验是在Semeval 2014和Twitter数据集上进行的，实验结果证实，通过我们的方法可以更好地建立方面和舆论单词之间的联系，因此图形注意力网络（GAT）的性能得到了显着提高。
Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.
