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Many algorithms have been implemented for the problem of text classification. Most of the work in this area was carried out for English text. Very little research has been carried out on Arabic text. The nature of Arabic text is different than that of English text, and preprocessing of Arabic text is more challenging. This paper presents an implementation of three automatic text-classification techniques for Arabic text. A corpus of 1445Arabic text documents belonging to nine categories has been automatically classified using the kNN, Rocchio, and naïve Bayes algorithms. The research results reveal that Naïve Bayes was the best performer, followed by kNN and Rocchio.

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We have put together a corpus of 242 abstracts of Arabic documents using the Proceedings of the Saudi Arabian National Conferences as a source. All these abstracts involve computer science and information systems. We also designed and built an automatic information retrieval system from scratch to handle Arabic data. The system was implemented in the C language using the GCC compiler and runs on IBM/PCs and compatible microcomputers. We have implemented both automatic and manual indexing techniques for this corpus. A long series of experiments using measures of recall and precision has demonstrated that automatic indexing is at least as effective as manual indexing and more effective in some cases. Since automatic indexing is both cheaper and faster, our results suggest that we can achieve a wider coverage of the literature with less money and produce as good results as with manual indexing. We have also compared the retrieval results using words as index terms versus stems and roots, and confirmed the results obtained by Al‐Kharashi and Abu‐Salem with smaller corpora that root indexing is more effective than word indexing. © 1997 John Wiley & Sons, Inc.

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