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A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

Maha Al-Yahya1, *

Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

* Corresponding Author: Maha Al-Yahya. Email: email.

Computers, Materials & Continua 2019, 60(2), 421-433. https://doi.org/10.32604/cmc.2019.06209

Abstract

The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of this work lies in the evaluation of the five machine learning methods for the task of genre identification for classical Arabic text using stylometric features.

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APA Style
, M.A. (2019). A comparative study of machine learning methods for genre identification of classical arabic text. Computers, Materials & Continua, 60(2), 421-433. https://doi.org/10.32604/cmc.2019.06209
Vancouver Style
MA. A comparative study of machine learning methods for genre identification of classical arabic text. Comput Mater Contin. 2019;60(2):421-433 https://doi.org/10.32604/cmc.2019.06209
IEEE Style
M.A. , "A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text," Comput. Mater. Contin., vol. 60, no. 2, pp. 421-433. 2019. https://doi.org/10.32604/cmc.2019.06209

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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