no code implementations • EACL (WANLP) 2021 • Mohamed Lichouri, Mourad Abbas, Besma Benaziz, Aicha Zitouni, Khaled Lounnas
The findings show that despite the simplicity of the proposed approach, using the LSVC model with a normalizing Arabic (NA) preprocessing and the BiLSTM architecture with an Embedding layer as input have yielded an encouraging F1score of 33. 71% and 57. 80% for sarcasm and sentiment detection, respectively.
no code implementations • SMM4H (COLING) 2020 • Mohamed Lichouri, Mourad Abbas
This paper describes our system developed for automatically classifying tweets that mention medications.
no code implementations • EACL (WANLP) 2021 • Mohamed Lichouri, Mourad Abbas, Khaled Lounnas, Besma Benaziz, Aicha Zitouni
In this paper, we analyze the impact of the weighted concatenation of TF-IDF features for the Arabic Dialect Identification task while we participated in the NADI2021 shared task.
no code implementations • COLING (WANLP) 2020 • Mohamed Lichouri, Mourad Abbas
In this paper, we present a description of our experiments on country-level Arabic dialect identification.
no code implementations • 16 Dec 2023 • Mohamed Lichouri, Khaled Lounnas, Aicha Zitouni, Houda Latrache, Rachida Djeradi
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.
no code implementations • WS 2019 • Mourad Abbas, Mohamed Lichouri, Abed Alhakim Freihat
This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task.