Fathy Elkazzaz


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BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts
Nsrin Ashraf | Fathy Elkazzaz | Mohamed Taha | Hamada Nayel | Tarek Elshishtawy
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes the systems submitted to iSarcasm shared task. The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text. Our team participated in iSarcasm for the Arabic language. A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection. In this model, a vector space TF-IDF has been used as for feature representation. The submitted system is simple and does not need any external resources. The test results show encouraging results.


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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.