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

• URL: http://arxiv.org/abs/2312.10536v1
• Date: Sat, 16 Dec 2023 20:23:53 GMT
• Title: USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification
• Authors: Mohamed Lichouri, Khaled Lounnas, Aicha Zitouni, Houda Latrache, Rachida Djeradi
• Abstract summary: We investigate the effects of surface preprocessing, morphological preprocessing, FastText vector model, and the weighted concatenation of TF-IDF features. During the evaluation phase, our system demonstrates noteworthy results, achieving an F1 score of 62.51%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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