Abstract
This paper describes a system for classification of Arabic poems according to the eras in which they were written. We used machine learning techniques where we applied a bunch of filters and classifiers. The best results were achieved by using the Multinomial Naïve Bayes (MNB) algorithm, with an accuracy equal to 70.21%, an F1-Score of 68.8% and a Kappa equal to 0.398, without filtering stop words. We observed that the stop words can have a positive impact on the accuracy but also a negative impact if it is used with word tokenizer preprocessing.
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Abbas, M., Lichouri, M., Zeggada, A. (2019). Classification of Arabic Poems: from the \(5^{th}\) to the \(15^{th}\) Century. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_18
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DOI: https://doi.org/10.1007/978-3-030-30754-7_18
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