@inproceedings{abbas-etal-2019-st,
title = "{ST} {MADAR} 2019 Shared Task: {A}rabic Fine-Grained Dialect Identification",
author = "Abbas, Mourad and
Lichouri, Mohamed and
Freihat, Abed Alhakim",
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/W19-4635",
doi = "10.18653/v1/W19-4635",
pages = "269--273",
abstract = "This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87 {\%} and 62.12 {\%} for both development and test sets.",
}
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<abstract>This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87 % and 62.12 % for both development and test sets.</abstract>
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%0 Conference Proceedings
%T ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification
%A Abbas, Mourad
%A Lichouri, Mohamed
%A Freihat, Abed Alhakim
%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 abbas-etal-2019-st
%X This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87 % and 62.12 % for both development and test sets.
%R 10.18653/v1/W19-4635
%U https://aclanthology.org/W19-4635
%U https://doi.org/10.18653/v1/W19-4635
%P 269-273
Markdown (Informal)
[ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification](https://aclanthology.org/W19-4635) (Abbas et al., WANLP 2019)
ACL