论文标题
利用立场层次结构，以检测网络文档的成本敏感立场
Exploiting stance hierarchies for cost-sensitive stance detection of Web documents
论文作者
论文摘要
在打击虚假新闻时，事实检查是一个必不可少的挑战。确定同意或不同意特定语句（索赔）的文档是此过程中的核心任务。在这种情况下，立场检测旨在将文件的立场（立场）确定为索赔。大多数方法通过4级分类模型来解决此任务，该模型高度不平衡。因此，即使这些情况对于诸如诸如事实检查的任务至关重要，他们在检测少数群体中（例如，“不同意”）尤其无效（例如，“不同意”）。在本文中，我们利用了立场类别的层次结构性质，这使我们能够提出级联二进制分类器的模块化管道，从而以每个步骤和类别的基础进行性能调整。我们通过结合神经和传统分类模型来实施我们的方法，这些模型突出了少数群体的错误分类成本。评估结果表明，我们的方法的最先进表现及其能够显着提高重要“不同意”类别的分类性能的能力。
Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. In this context, stance detection aims at identifying the position (stance) of a document towards a claim. Most approaches address this task through a 4-class classification model where the class distribution is highly imbalanced. Therefore, they are particularly ineffective in detecting the minority classes (for instance, 'disagree'), even though such instances are crucial for tasks such as fact-checking by providing evidence for detecting false claims. In this paper, we exploit the hierarchical nature of stance classes, which allows us to propose a modular pipeline of cascading binary classifiers, enabling performance tuning on a per step and class basis. We implement our approach through a combination of neural and traditional classification models that highlight the misclassification costs of minority classes. Evaluation results demonstrate state-of-the-art performance of our approach and its ability to significantly improve the classification performance of the important 'disagree' class.
