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arXiv · 2312.10536

USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification

Mohamed Lichouri·Khaled Lounnas·Aicha Zitouni·Houda Latrache·Rachida Djeradi
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Abstract

In this paper, we conduct an in-depth analysis of several key factors influencing the performance of Arabic Dialect Identification NADI'2023, with a specific focus on the first subtask involving country-level dialect identification. Our investigation encompasses the effects of surface preprocessing, morphological preprocessing, FastText vector model, and the weighted concatenation of TF-IDF features. For classification purposes, we employ the Linear Support Vector Classification (LSVC) model. During the evaluation phase, our system demonstrates noteworthy results, achieving an F1 score of 62.51%. This achievement closely aligns with the average F1 scores attained by other systems submitted for the first subtask, which stands at 72.91%.

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Mohamed Lichouri, Khaled Lounnas, Aicha Zitouni, Houda Latrache, Rachida Djeradi. 2023-12-16. USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification. https://arxiv.org/abs/2312.10536

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