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        <title>Arabic Dialects Identification for All Arabic countries</title>
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        <namePart type="given">Hawraa</namePart>
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        <namePart type="given">Zena</namePart>
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            <title>Proceedings of the Fifth Arabic Natural Language Processing Workshop</title>
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        <name type="personal">
            <namePart type="given">Imed</namePart>
            <namePart type="family">Zitouni</namePart>
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            <namePart type="given">Muhammad</namePart>
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            <namePart type="given">Mahmoud</namePart>
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    <abstract>Arabic dialects are among of three main variant of Arabic language (Classical Arabic, modern standard Arabic and dialectal Arabic). It has many variants according to the country, city (provinces) or town. In this paper, several techniques with multiple algorithms are applied for Arabic dialects identification starting from removing noise till classification task using all Arabic countries as 21 classes. Three types of classifiers (Naïve Bayes, Logistic Regression, and Decision Tree) are combined using voting with two different methodologies. Also clustering technique is used for decreasing the noise that result from the existing of MSA tweets in the data set for training phase. The results of f-measure were 27.17, 41.34 and 52.38 for first methodology without clustering, second methodology without clustering, and second methodology with clustering, the used data set is NADI shared task data set.</abstract>
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%0 Conference Proceedings
%T Arabic Dialects Identification for All Arabic countries
%A Aliwy, Ahmed
%A Taher, Hawraa
%A AboAltaheen, Zena
%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 aliwy-etal-2020-arabic
%X Arabic dialects are among of three main variant of Arabic language (Classical Arabic, modern standard Arabic and dialectal Arabic). It has many variants according to the country, city (provinces) or town. In this paper, several techniques with multiple algorithms are applied for Arabic dialects identification starting from removing noise till classification task using all Arabic countries as 21 classes. Three types of classifiers (Naïve Bayes, Logistic Regression, and Decision Tree) are combined using voting with two different methodologies. Also clustering technique is used for decreasing the noise that result from the existing of MSA tweets in the data set for training phase. The results of f-measure were 27.17, 41.34 and 52.38 for first methodology without clustering, second methodology without clustering, and second methodology with clustering, the used data set is NADI shared task data set.
%U https://aclanthology.org/2020.wanlp-1.32
%P 302-307

Markdown (Informal)

[Arabic Dialects Identification for All Arabic countries](https://aclanthology.org/2020.wanlp-1.32) (Aliwy et al., WANLP 2020)

• Arabic Dialects Identification for All Arabic countries (Aliwy et al., WANLP 2020)

ACL

• Ahmed Aliwy, Hawraa Taher, and Zena AboAltaheen. 2020. Arabic Dialects Identification for All Arabic countries. In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 302–307, Barcelona, Spain (Online). Association for Computational Linguistics.