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A machine learning approach for Arabic text classification using N-gram frequency statistics

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  • Khreisat, Laila

Abstract

In this paper a machine learning approach for classifying Arabic text documents is presented. To handle the high dimensionality of text documents, embeddings are used to map each document (instance) into R (the set of real numbers) representing the tri-gram frequency statistics profiles for a document. Classification is achieved by computing a dissimilarity measure, called the Manhattan distance, between the profile of the instance to be classified and the profiles of all the instances in the training set. The class (category) to which an instance (document) belongs is the one with the least computed Manhattan measure. The Dice similarity measure is used to compare the performance of method. Results show that tri-gram text classification using the Dice measure outperforms classification using the Manhattan measure.

Suggested Citation

  • Khreisat, Laila, 2009. "A machine learning approach for Arabic text classification using N-gram frequency statistics," Journal of Informetrics, Elsevier, vol. 3(1), pages 72-77.
    • Handle: RePEc:eee:infome:v:3:y:2009:i:1:p:72-77
    DOI: 10.1016/j.joi.2008.11.005
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    References listed on IDEAS

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    1. 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.
    2. 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:

    1. 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).
    2. 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.
    3. 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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