@inproceedings{fares-etal-2019-arabic,
title = "{A}rabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features",
author = "Fares, Youssef and
El-Zanaty, Zeyad and
Abdel-Salam, Kareem and
Ezzeldin, Muhammed and
Mohamed, Aliaa and
El-Awaad, Karim and
Torki, Marwan",
editor = "El-Hajj, Wassim and
Belguith, Lamia Hadrich and
Bougares, Fethi and
Magdy, Walid and
Zitouni, Imed and
Tomeh, Nadi and
El-Haj, Mahmoud and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4626",
doi = "10.18653/v1/W19-4626",
pages = "224--228",
abstract = "Studies on Dialectical Arabic are growing more important by the day as it becomes the primary written and spoken form of Arabic online in informal settings. Among the important problems that should be explored is that of dialect identification. This paper reports different techniques that can be applied towards such goal and reports their performance on the Multi Arabic Dialect Applications and Resources (MADAR) Arabic Dialect Corpora. Our results show that improving on traditional systems using frequency based features and non deep learning classifiers is a challenging task. We propose different models based on different word and document representations. Our top model is able to achieve an F1 macro averaged score of 65.66 on MADAR{'}s small-scale parallel corpus of 25 dialects and Modern Standard Arabic (MSA).",
}
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%0 Conference Proceedings
%T Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features
%A Fares, Youssef
%A El-Zanaty, Zeyad
%A Abdel-Salam, Kareem
%A Ezzeldin, Muhammed
%A Mohamed, Aliaa
%A El-Awaad, Karim
%A Torki, Marwan
%Y El-Hajj, Wassim
%Y Belguith, Lamia Hadrich
%Y Bougares, Fethi
%Y Magdy, Walid
%Y Zitouni, Imed
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Fourth Arabic Natural Language Processing Workshop
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F fares-etal-2019-arabic
%X Studies on Dialectical Arabic are growing more important by the day as it becomes the primary written and spoken form of Arabic online in informal settings. Among the important problems that should be explored is that of dialect identification. This paper reports different techniques that can be applied towards such goal and reports their performance on the Multi Arabic Dialect Applications and Resources (MADAR) Arabic Dialect Corpora. Our results show that improving on traditional systems using frequency based features and non deep learning classifiers is a challenging task. We propose different models based on different word and document representations. Our top model is able to achieve an F1 macro averaged score of 65.66 on MADAR’s small-scale parallel corpus of 25 dialects and Modern Standard Arabic (MSA).
%R 10.18653/v1/W19-4626
%U https://aclanthology.org/W19-4626
%U https://doi.org/10.18653/v1/W19-4626
%P 224-228
Markdown (Informal)
[Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features](https://aclanthology.org/W19-4626) (Fares et al., WANLP 2019)
ACL