@inproceedings{nayel-2020-nayel,
    title = "{NAYEL} at {S}em{E}val-2020 Task 12: {TF}/{IDF}-Based Approach for Automatic Offensive Language Detection in {A}rabic Tweets",
    author = "Nayel, Hamada",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.semeval-1.276/",
    doi = "10.18653/v1/2020.semeval-1.276",
    pages = "2086--2089",
    abstract = "In this paper, we present the system submitted to ``SemEval-2020 Task 12''. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm. Our model reported 84.20{\%}, 81.82{\%} f1-score on development set and test set respectively. The best performed system and the system in the last rank reported 90.17{\%} and 44.51{\%} f1-score on test set respectively."
}