论文标题
具有语义特征提取和游戏理论粗糙集的讽刺新闻检测
Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets
论文作者
论文摘要
讽刺新闻检测是防止错误信息传播的重要但艰巨的任务。已经提出并提供了许多基于功能的基于功能和端到端神经网络的讽刺新闻检测系统并提供了令人鼓舞的结果。现有方法探讨了讽刺新闻文章中的全面单词特征，但是使用词向量的sweet形成讽刺新闻，缺乏语义指标。此外，讽刺和新闻模仿的模糊性决定，新闻推文几乎不能用二进制决定来归类，即讽刺或合法。为了解决这些问题，我们收集讽刺和合法的新闻推文，并提出基于语义功能的方法。通过探索短语，实体以及主要条款和相对条款之间的不一致来提取特征。我们应用游戏理论粗糙集模型来检测讽刺新闻，其中概率阈值是通过游戏平衡和重复学习机制得出的。收集到的数据集的实验结果表明，与Pawlak Rough Set模型和SVM相比，所提出的方法的鲁棒性和改进。
Satirical news detection is an important yet challenging task to prevent spread of misinformation. Many feature based and end-to-end neural nets based satirical news detection systems have been proposed and delivered promising results. Existing approaches explore comprehensive word features from satirical news articles, but lack semantic metrics using word vectors for tweet form satirical news. Moreover, the vagueness of satire and news parody determines that a news tweet can hardly be classified with a binary decision, that is, satirical or legitimate. To address these issues, we collect satirical and legitimate news tweets, and propose a semantic feature based approach. Features are extracted by exploring inconsistencies in phrases, entities, and between main and relative clauses. We apply game-theoretic rough set model to detect satirical news, in which probabilistic thresholds are derived by game equilibrium and repetition learning mechanism. Experimental results on the collected dataset show the robustness and improvement of the proposed approach compared with Pawlak rough set model and SVM.
