A machine learning approach for Arabic text classification using N-gram frequency statistics
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DOI: 10.1016/j.joi.2008.11.005
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References listed on IDEAS
- Rehab M. Duwairi, 2006. "Machine learning for Arabic text categorization," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1005-1010, June.
- Leo Egghe, 2000. "The Distribution of N-Grams," Scientometrics, Springer;Akadémiai Kiadó, vol. 47(2), pages 237-252, February.
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Cited by:
- Volkovich, Zeev & Granichin, Oleg & Redkin, Oleg & Bernikova, Olga, 2016. "Modeling and visualization of media in Arabic," Journal of Informetrics, Elsevier, vol. 10(2), pages 439-453.
- Hornik, Kurt & Mair, Patrick & Rauch, Johannes & Geiger, Wilhelm & Buchta, Christian & Feinerer, Ingo, 2013. "The textcat Package for n-Gram Based Text Categorization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i06).
- Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).
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Keywords
Data mining; Classification; Categorization; Arabic; N-gram; Machine learning;All these keywords.
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