Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection
Carla Perez Almendros, Luis Espinosa-Anke, Steven Schockaert · 2019
This paper summarizes our contribution to the Hyperpartisan News Detection task in Se-mEval 2019.We experiment with two different approaches: 1) an SVM classifier based on word vector averages and hand-crafted linguistic features, and 2) a BiLSTM-based neural text classifier trained on a filtered training set.Surprisingly, despite their different nature, both approaches achieve an accuracy of 0.74.The main focus of this paper is to further analyze the remarkable fact that a simple featurebased approach can perform on par with modern neural classifiers.We also highlight the effectiveness of our filtering strategy for training the neural network on a large but noisy training set.