Houda Latrache


2023

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USTHB at ArAIEval’23 Shared Task: Disinformation Detection System based on Linguistic Feature Concatenation
Mohamed Lichouri | Khaled Lounnas | Aicha Zitouni | Houda Latrache | Rachida Djeradi
Proceedings of ArabicNLP 2023

In this research paper, we undertake a comprehensive examination of several pivotal factors that impact the performance of Arabic Disinformation Detection in the ArAIEval’2023 shared task. Our exploration encompasses the influence of surface preprocessing, morphological preprocessing, the FastText vector model, and the weighted fusion of TF-IDF features. To carry out classification tasks, we employ the Linear Support Vector Classification (LSVC) model. In the evaluation phase, our system showcases significant results, achieving an F1 micro score of 76.70% and 50.46% for binary and multiple classification scenarios, respectively. These accomplishments closely correspond to the average F1 micro scores achieved by other systems submitted for the second subtask, standing at 77.96% and 64.85% for binary and multiple classification scenarios, respectively.

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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
Proceedings of ArabicNLP 2023

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%.