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Automated Arabic text categorization using SVM and NB
Author
Source
International Arab Journal of E-Technology
Issue
Vol. 2, Issue 2 (30 Jun. 2011), pp.124-128, 5 p.
Publisher
Publication Date
2011-06-30
Country of Publication
Jordan
No. of Pages
5
Main Subjects
Languages & Comparative Literature
Topics
Abstract EN
Text classification is a supervised learning technique that uses labeled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier.
In this paper, we investigate Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets.
The bases of our comparison are the most popular text evaluation measures.
The Experimental results against different Arabic text categorization data sets reveal that SVM algorithm outperforms the NB with regards to all measures.
American Psychological Association (APA)
al-Salim, Salih. 2011. Automated Arabic text categorization using SVM and NB. International Arab Journal of E-Technology،Vol. 2, no. 2, pp.124-128.
https://search.emarefa.net/detail/BIM-266887
Modern Language Association (MLA)
al-Salim, Salih. Automated Arabic text categorization using SVM and NB. International Arab Journal of E-Technology Vol. 2, no. 2 (Jun. 2011), pp.124-128.
https://search.emarefa.net/detail/BIM-266887
American Medical Association (AMA)
al-Salim, Salih. Automated Arabic text categorization using SVM and NB. International Arab Journal of E-Technology. 2011. Vol. 2, no. 2, pp.124-128.
https://search.emarefa.net/detail/BIM-266887
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 127-128
Record ID
BIM-266887