Arabic text categorization
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 4, Issue 2 (30 Apr. 2007), pp.125-131, 7 p.
Publisher
Publication Date
2007-04-30
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
In this paper, we compare the performance of three classifiers for Arabic text categorization.
In particular, the naïve Bayes, k-nearest-neighbors (knn), and distance-based classifiers were used.
Unclassified documents were preprocessed by removing punctuation marks and stop words.
Each document is then represented as a vector of words (or of words and their frequencies as in the case of the naïve Bayes classifier).
Stemming was used to reduce the dimensionality of feature vectors of documents.
The accuracy of the classifiers is compared using recall, precision, error rate and fallout.
The results of the experimentations that were carried out on an in-house collected Arabic text show that the naïve Bayes classifier outperforms the other two.
American Psychological Association (APA)
al-Duwayri, Rehab. 2007. Arabic text categorization. The International Arab Journal of Information Technology،Vol. 4, no. 2, pp.125-131.
https://search.emarefa.net/detail/BIM-11633
Modern Language Association (MLA)
al-Duwayri, Rehab. Arabic text categorization. The International Arab Journal of Information Technology Vol. 4, no. 2 (Apr. 2007), pp.125-131.
https://search.emarefa.net/detail/BIM-11633
American Medical Association (AMA)
al-Duwayri, Rehab. Arabic text categorization. The International Arab Journal of Information Technology. 2007. Vol. 4, no. 2, pp.125-131.
https://search.emarefa.net/detail/BIM-11633
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 129-130
Record ID
BIM-11633