@inproceedings{rusert-srinivasan-2019-nlp,
    title = "{NLP}@{UIOWA} at {S}em{E}val-2019 Task 6: Classifying the Crass using Multi-windowed {CNN}s",
    author = "Rusert, Jonathan  and
      Srinivasan, Padmini",
    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/S19-2125/",
    doi = "10.18653/v1/S19-2125",
    pages = "704--711",
    abstract = "This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories. Our system is a text based CNN, which learns only from the provided training data. Our system achieves 80 - 90{\%} accuracy for the binary classification problems (offensive vs not offensive and targeted vs untargeted) and 63{\%} accuracy for trinary classification (group vs individual vs other)."
}