@inproceedings{rani-ojha-2019-kmi,
    title = "{KMI}-Coling at {S}em{E}val-2019 Task 6: Exploring N-grams for Offensive Language detection",
    author = "Rani, Priya  and
      Ojha, Atul Kr.",
    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/S19-2119/",
    doi = "10.18653/v1/S19-2119",
    pages = "668--671",
    abstract = "In this paper, we present the system description of Offensive language detection tool which is developed by the KMI{\_}Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted in SemEval 2019 workshop. To develop the system, we have explored n-grams up to 8-gram and trained three different namely A, B and C systems for three different subtasks within the OffensEval task which achieves 79.76{\%}, 87.91{\%} and 44.37{\%} accuracy respectively. The task was completed using the dataset provided to us by the OffensEval organisers was the part of OLID dataset. It consists of 13,240 tweets extracted from twitter and were annotated at three levels using crowdsourcing."
}