UTMN at SemEval-2020 Task 11: A Kitchen Solution to Automatic Propaganda Detection
This work addresses propaganda detection for NLP applications, but it is incremental as it builds on existing methods with limited performance gains.
The paper tackled automatic propaganda detection in text by developing a fast solution using feature adjustment and Logistic Regression, achieving an F-score of 0.37 at SemEval-2020 Task 11.
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.