论文标题
与动态异质图神经网络共同建模方面和情感
Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks
论文作者
论文摘要
基于目标的情感分析旨在检测对它们的意见方面（方面提取）和情感极性（情感检测）。先前的管道和集成方法都无法精确地建模这两个目标之间的先天连接。在本文中，我们提出了一个新型的动态异质图，以明确的方式共同对两个目标进行建模。普通的单词和情感标签都被视为异质图中的节点，以便各个方面可以与情感信息相互作用。该图以多种类型的依赖项初始化，并在实时预测期间动态修改。基准数据集上的实验表明，我们的模型优于最新模型。进一步的分析表明，我们的模型在多开的方面和无开放方面的情况下在具有挑战性的实例上获得了显着的绩效增长。
Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them. Both the previous pipeline and integrated methods fail to precisely model the innate connection between these two objectives. In this paper, we propose a novel dynamic heterogeneous graph to jointly model the two objectives in an explicit way. Both the ordinary words and sentiment labels are treated as nodes in the heterogeneous graph, so that the aspect words can interact with the sentiment information. The graph is initialized with multiple types of dependencies, and dynamically modified during real-time prediction. Experiments on the benchmark datasets show that our model outperforms the state-of-the-art models. Further analysis demonstrates that our model obtains significant performance gain on the challenging instances under multiple-opinion aspects and no-opinion aspect situations.
