@inproceedings{kannan-stein-2019-tukast,
    title = {{T}{\"u}{K}a{S}t at {S}em{E}val-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification},
    author = "Kannan, Madeeswaran  and
      Stein, Lukas",
    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/jlcl-multiple-ingestion/S19-2134/",
    doi = "10.18653/v1/S19-2134",
    pages = "763--769",
    abstract = {We describe our system (T{\"u}KaSt) submitted for Task 6: Offensive Language Classification, at SemEval 2019. We developed multiple SVM classifier models that used sentence-level dense vector representations of tweets enriched with sentiment information and term-weighting. Our best results achieved F1 scores of 0.734, 0.660 and 0.465 in the first, second and third sub-tasks respectively. We also describe a neural network model that was developed in parallel but not used during evaluation due to time constraints.}
}