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The Effect of Preprocessing on Arabic Document Categorization

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School of Information Science and Engineering, Central south University, Changsha 410000, China
2
College of Computer Science and Electrical Engineering, Hunan University, Changsha 410000, China
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Author to whom correspondence should be addressed.
Academic Editor: Tom Burr
Algorithms 2016, 9(2), 27; https://doi.org/10.3390/a9020027
Received: 21 January 2016 / Revised: 30 March 2016 / Accepted: 12 April 2016 / Published: 18 April 2016
Preprocessing is one of the main components in a conventional document categorization (DC) framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM). Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks. View Full-Text
Keywords: document categorization; text preprocessing; stemming techniques; classification techniques; Arabic language processing document categorization; text preprocessing; stemming techniques; classification techniques; Arabic language processing
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Ayedh, A.; TAN, G.; Alwesabi, K.; Rajeh, H. The Effect of Preprocessing on Arabic Document Categorization. Algorithms 2016, 9, 27.

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