ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification

Mourad Abbas, Mohamed Lichouri, Abed Alhakim Freihat


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.
Anthology ID:
W19-4635
Volume:
Proceedings of the Fourth Arabic Natural Language Processing Workshop
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WANLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
269–273
Language:
URL:
https://www.aclweb.org/anthology/W19-4635
DOI:
10.18653/v1/W19-4635
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-4635.pdf