@inproceedings{elkazzaz-etal-2021-bfcai,
    title = "{BFCAI} at {C}om{MA}@{ICON} 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification",
    author = "Elkazzaz, Fathy  and
      Sakr, Fatma  and
      Orban, Rasha  and
      Nayel, Hamada",
    editor = "Kumar, Ritesh  and
      Singh, Siddharth  and
      Nandi, Enakshi  and
      Ratan, Shyam  and
      Devi, Laishram Niranjana  and
      Lahiri, Bornini  and
      Bansal, Akanksha  and
      Bhagat, Akash  and
      Dawer, Yogesh",
    booktitle = "Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification",
    month = dec,
    year = "2021",
    address = "NIT Silchar",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.icon-multigen.11/",
    pages = "70--74",
    abstract = "This paper presents the system that has been submitted to the multilingual gender biased and communal language identification shared task by BFCAI team. The proposed model used Support Vector Machines (SVMs) as a classification algorithm. The features have been extracted using TF/IDF model with unigram and bigram. The proposed model is very simple and there are no external resources are needed to build the model."
}