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Proceedings Paper

An experimental study for Arabic text classification techniques
Author(s): Bassam Al-Shargabi; Fekry Olayah
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Paper Abstract

Several algorithms have been implemented to resolve the problem of text categorization. Most of the work in this area geared for English text, whereas few researches have been conducted on Arabic text. However, the nature of Arabic text is different than English text; pre-processing of Arabic text are more challenging. In this paper an experimental study was conducted on three techniques for Arabic text classification; these techniques, Discriminative Multinominal Naive Bayes (DMNB), Naïve Bayesian (NB) and IBK Algorithms, The paper aimed to assess the accuracy for each classifier and to determine which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text.

Paper Details

Date Published: 2 June 2012
PDF: 5 pages
Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83340H (2 June 2012); doi: 10.1117/12.946039
Show Author Affiliations
Bassam Al-Shargabi, Al-Isra Univ. (Jordan)
Fekry Olayah, Najran Univ. (Saudi Arabia)


Published in SPIE Proceedings Vol. 8334:
Fourth International Conference on Digital Image Processing (ICDIP 2012)
Mohamed Othman; Sukumar Senthilkumar; Xie Yi, Editor(s)

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