Fatma Sakr

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2021

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BFCAI at ComMA@ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification
Fathy Elkazzaz | Fatma Sakr | Rasha Orban | Hamada Nayel
Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification

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.
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