@InProceedings{papadopoulou-EtAl:2019:S19-2,
  author    = {Papadopoulou, Olga  and  Kordopatis-Zilos, Giorgos  and  Zampoglou, Markos  and  Papadopoulos, Symeon  and  Kompatsiaris, Yiannis},
  title     = {Brenda Starr at SemEval-2019 Task 4: 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     = {924--928},
  abstract  = {In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard.},
  url       = {http://www.aclweb.org/anthology/S19-2157}
}