@inproceedings{zhu-etal-2019-um,
    title = "{UM}-{IU}@{LING} at {S}em{E}val-2019 Task 6: Identifying Offensive Tweets Using {BERT} and {SVM}s",
    author = {Zhu, Jian  and
      Tian, Zuoyu  and
      K{\"u}bler, Sandra},
    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/ingest-emnlp/S19-2138/",
    doi = "10.18653/v1/S19-2138",
    pages = "788--795",
    abstract = "This paper describes the UM-IU@LING{'}s system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions."
}