@InProceedings{joo-hwang:2019:S19-2,
  author    = {Joo, Youngjun  and  Hwang, Inchon},
  title     = {Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {990--994},
  abstract  = {This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A.},
  url       = {http://www.aclweb.org/anthology/S19-2171}
}