论文标题
UTMN在Semeval-2020任务11：自动宣传检测的厨房解决方案
UTMN at SemEval-2020 Task 11: A Kitchen Solution to Automatic Propaganda Detection
论文作者
论文摘要
该文章介绍了基于onFeature调整的Semeval-2020任务11上宣传检测的快速解决方案。我们使用功能的近语矢量化和简单的逻辑回归频道来快速测试有关我们数据的不同假设。我们提出了似乎最好的解决方案，但是，我们无法将其与任务理论组织提出的指标的结果保持一致。我们通过在训练集中使用两个类别的样本（宣传和无）的样本来测试我们的系统如何处理类别和功能失衡，即在上下文中的大小，在上下文的大小中，将令牌的矢量化和矢量化手段的组合结合在一起。 SEMEVAL2020任务11的OURSYSTEM的结果为F-评分= 0.37。
The article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based onfeature adjustment. We use per-token vectorization of features and a simple Logistic Regressionclassifier to quickly test different hypotheses about our data. We come up with what seems to usthe best solution, however, we are unable to align it with the result of the metric suggested by theorganizers of the task. We test how our system handles class and feature imbalance by varying thenumber of samples of two classes (Propaganda and None) in the training set, the size of a contextwindow in which a token is vectorized and combination of vectorization means. The result of oursystem at SemEval2020 Task 11 is F-score=0.37.
