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%.

该研究针对阿拉伯方言识别的国家级方言识别这一子任务，对表面预处理、形态预处理、FastText 向量模型和 TF-IDF 特征的加权拼接等多个关键因素进行深入分析，使用线性支持向量分类（LSVC）模型进行分类，并取得了显著成果，F1 得分为 62.51%，接近于该子任务所提交其他系统的平均 F1 得分 72.91%。