@InProceedings{srivastava-EtAl:2019:S19-2,
  author    = {Srivastava, Vertika  and  Gupta, Ankita  and  Prakash, Divya  and  Sahoo, Sudeep Kumar  and  R.R, Rohit  and  Kim, Yeon Hyang},
  title     = {Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1078--1082},
  abstract  = {In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person. Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0\% accuracy) and 1st rank on the secondary metric (82.1\% F1-score), among all participating teams. Comparison with the best performing system on the leaderboard shows that our system is behind by only 0.2\% absolute difference in accuracy.},
  url       = {http://www.aclweb.org/anthology/S19-2189}
}