论文标题
王国：知识引导的领域适应情感分析
KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis
论文作者
论文摘要
近年来，跨域情绪分析受到了极大的关注，这是由于需要在使用情感分析的不同应用程序之间打击域间隙。在本文中，我们通过探索外常识性知识的作用来对这项任务进行新颖的看法。我们介绍了一个新的框架王国，该框架利用概念网知识图来丰富文档的语义，通过提供特定于领域的和域名的背景概念。这些概念是通过训练图形卷积自动编码器来学习的，该卷积自动编码器以域名不变的方式利用域间概念。通过这些学识渊博的概念来调节流行的域 - 交流基线方法有助于提高其在最先进的方法上的性能，从而证明我们提出的框架的功效。
Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework.
