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A comparison study of some Arabic root finding algorithms

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  • Emad Al‐Shawakfa
  • Amer Al‐Badarneh
  • Safwan Shatnawi
  • Khaleel Al‐Rabab'ah
  • Basel Bani‐Ismail

Abstract

Arabic has a complex structure, which makes it difficult to apply natural language processing (NLP). Much research on Arabic NLP (ANLP) does exist; however, it is not as mature as that of other languages. Finding Arabic roots is an important step toward conducting effective research on most of ANLP applications. The authors have studied and compared six root‐finding algorithms with success rates of over 90%. All algorithms of this study did not use the same testing corpus and/or benchmarking measures. They unified the testing process by implementing their own algorithm descriptions and building a corpus out of 3823 triliteral roots, applying 73 triliteral patterns, and with 18 affixes, producing around 27.6 million words. They tested the algorithms with the generated corpus and have obtained interesting results; they offer to share the corpus freely for benchmarking and ANLP research.

Suggested Citation

  • Emad Al‐Shawakfa & Amer Al‐Badarneh & Safwan Shatnawi & Khaleel Al‐Rabab'ah & Basel Bani‐Ismail, 2010. "A comparison study of some Arabic root finding algorithms," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(5), pages 1015-1024, May.
    • Handle: RePEc:bla:jamist:v:61:y:2010:i:5:p:1015-1024
    DOI: 10.1002/asi.21301
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    References listed on IDEAS

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    1. Ibrahim A. Al‐Kharashi & Martha W. Evens, 1994. "Comparing words, stems, and roots as index terms in an Arabic Information Retrieval system," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 45(8), pages 548-560, September.
    2. 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.
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