@InProceedings{perezalmendros-espinosaanke-schockaert:2019:S19-2,
  author    = {Perez Almendros, Carla  and  Espinosa Anke, Luis  and  Schockaert, Steven},
  title     = {Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {929--933},
  abstract  = {This paper summarizes our contribution to the Hyperpartisan News Detection task in SemEval 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 feature-based 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.},
  url       = {http://www.aclweb.org/anthology/S19-2158}
}