Identifying political perspectives in news media has become an important task
due to the rapid growth of political commentary and the increasingly polarized
political ideologies. Previous approaches focus on textual content and leave
out the rich social and political context that is essential in the perspective
detection process. To address this limitation, we propose KGAP, a political
perspective detection method that incorporates external domain knowledge.
Specifically, we construct a political knowledge graph to serve as
domain-specific external knowledge. We then construct heterogeneous information
networks to represent news documents, which jointly model news text and
external knowledge. Finally, we adopt relational graph neural networks and
conduct political perspective detection as graph-level classification.
Extensive experiments demonstrate that our method consistently achieves the
best performance on two real-world perspective detection benchmarks. Ablation
studies further bear out the necessity of external knowledge and the
effectiveness of our graph-based approach.

本文提出了一种利用外部领域知识进行政治观点识别的 KGAP 方法，该方法在构造政治知识图谱的基础上，结合异构信息网络来表示新闻文本与外部知识，并采用关系图神经网络进行政治观点检测，实验结果表明该方法在现实世界的识别标准中表现最佳。