Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection

Abstract

This paper describes our submission1 to the SemEval 2019 Hyperpartisan News Detection task. Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining good performance. To this goal, we follow a feature-based approach and perform several experiments with different machine learning classifiers. On the main task, our model achieved an accuracy of 71.7%, which was improved after the task's end to 72.9%. We also participate in the meta-learning sub-task, for classifying documents with the binary classifications of all submitted systems as input, achieving an accuracy of 89.9%.