@inproceedings{yeh-etal-2019-tom,
    title = "Tom Jumbo-Grumbo at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection with {G}lo{V}e vectors and {SVM}",
    author = "Yeh, Chia-Lun  and
      Loni, Babak  and
      Schuth, Anne",
    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/S19-2187/",
    doi = "10.18653/v1/S19-2187",
    pages = "1067--1071",
    abstract = "In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60{\%} to near 80{\%}. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility."
}