@inproceedings{joo-hwang-2019-steve,
    title = "Steve {M}artin at {S}em{E}val-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News",
    author = "Joo, Youngjun  and
      Hwang, Inchon",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S19-2171/",
    doi = "10.18653/v1/S19-2171",
    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."
}