@inproceedings{younes-etal-2020-team,
title = "Team {A}lexa at {NADI} Shared Task",
author = "Younes, Mutaz and
Al-khdour, Nour and
AL-Smadi, Mohammad",
editor = "Zitouni, Imed and
Abdul-Mageed, Muhammad and
Bouamor, Houda and
Bougares, Fethi and
El-Haj, Mahmoud and
Tomeh, Nadi and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wanlp-1.22",
pages = "237--242",
abstract = "In this paper, we discuss our team{'}s work on the NADI Shared Task. The task requires classifying Arabic tweets among 21 dialects. We tested out different approaches, and the best one was the simplest one. Our best submission was using Multinational Naive Bayes (MNB) classifier (Small and Hsiao, 1985) with n-grams as features. Despite its simplicity, this classifier shows better results than complicated models such as BERT. Our best submitted score was 17{\%} F1-score and 35{\%} accuracy.",
}
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<abstract>In this paper, we discuss our team’s work on the NADI Shared Task. The task requires classifying Arabic tweets among 21 dialects. We tested out different approaches, and the best one was the simplest one. Our best submission was using Multinational Naive Bayes (MNB) classifier (Small and Hsiao, 1985) with n-grams as features. Despite its simplicity, this classifier shows better results than complicated models such as BERT. Our best submitted score was 17% F1-score and 35% accuracy.</abstract>
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%0 Conference Proceedings
%T Team Alexa at NADI Shared Task
%A Younes, Mutaz
%A Al-khdour, Nour
%A AL-Smadi, Mohammad
%Y Zitouni, Imed
%Y Abdul-Mageed, Muhammad
%Y Bouamor, Houda
%Y Bougares, Fethi
%Y El-Haj, Mahmoud
%Y Tomeh, Nadi
%Y Zaghouani, Wajdi
%S Proceedings of the Fifth Arabic Natural Language Processing Workshop
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F younes-etal-2020-team
%X In this paper, we discuss our team’s work on the NADI Shared Task. The task requires classifying Arabic tweets among 21 dialects. We tested out different approaches, and the best one was the simplest one. Our best submission was using Multinational Naive Bayes (MNB) classifier (Small and Hsiao, 1985) with n-grams as features. Despite its simplicity, this classifier shows better results than complicated models such as BERT. Our best submitted score was 17% F1-score and 35% accuracy.
%U https://aclanthology.org/2020.wanlp-1.22
%P 237-242
Markdown (Informal)
[Team Alexa at NADI Shared Task](https://aclanthology.org/2020.wanlp-1.22) (Younes et al., WANLP 2020)
ACL
- Mutaz Younes, Nour Al-khdour, and Mohammad AL-Smadi. 2020. Team Alexa at NADI Shared Task. In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 237–242, Barcelona, Spain (Online). Association for Computational Linguistics.