Tarek Elshishtawy


2024

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BFCI at AraFinNLP2024: Support Vector Machines for Arabic Financial Text Classification
Nsrin Ashraf | Hamada Nayel | Mohammed Aldawsari | Hosahalli Shashirekha | Tarek Elshishtawy
Proceedings of The Second Arabic Natural Language Processing Conference

In this paper, a description of the system submitted by BFCAI team to the AraFinNLP2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines outperformed all other algorithms with an f-score of 49%. Our submission’s result is appropriate compared to the simplicity of the proposed model’s structure.

2022

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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.