@inproceedings{doostmohammadi-etal-2019-ghmerti,
    title = "Ghmerti at {S}em{E}val-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification",
    author = "Doostmohammadi, Ehsan  and
      Sameti, Hossein  and
      Saffar, Ali",
    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/S19-2110/",
    doi = "10.18653/v1/S19-2110",
    pages = "617--621",
    abstract = "This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model is 77.93{\%} macro-averaged F1-score."
}