Abstract
In context of Arabic, the authorship attribution (AA) problem is not addressed well comparing with other natural languages such English, Chinese and Dutch. This paper addresses the attribution problem in context of Islamic fatwā’. To the best of our knowledge, this is the first study of its kind that addresses this problem in such domain. In term of attribution methods, three machine-learning classifiers namely, the locally weighted learning (LWL) classifier, decision tree C4.5, and Random Forest (RF) are used. The experiment is performed with a selected list of stylomatric features. To extract the most discriminating features, various feature selection techniques are used. The experimental results show that the classifiers have different behaviour respect each feature reduction techniques. Among the used classifiers, the C4.5 method gives the best accuracy.
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Al-Sarem, M., Emara, AH. (2019). Analysis the Arabic Authorship Attribution Using Machine Learning Methods: Application on Islamic Fatwā. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_21
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DOI: https://doi.org/10.1007/978-3-319-99007-1_21
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