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Enhanced Arabic Information Retrieval: Light Stemming and Stop Words

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Soft Computing Applications and Intelligent Systems (M-CAIT 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 378))

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Abstract

Stemming is a process of reducing inflected words to their stem, base or root from a generally written word form. For languages that is high inflected like Arabic. Stemming improve the retrieval performance by reducing words variants. The effectiveness of stop words lists with light stemming for Arabic information retrieval (General stopwords list, Khoja stopwords list, Combined stopwords list), were investigated in this paper. Using vector space model as the popular weighting scheme was examined. The idea is to combine (General and Khoja) stopwords lists with light stemming to enhance the performance, and compare their effects on retrieval. The Linguistic Data Consortium (LDC) Arabic Newswire data set was used. The best performance was achieved with the Combined stopwords list, with light stemming.

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Author information

Authors and Affiliations

  1. Faculity of Information Science and Technology, Universiti Kebangsaan Malaysia, Malaysia, Bangi

    Jaffar Atwan & Masnizah Mohd

  2. Faculty of Computer Science and Informatics, Amman Arab University, Amman, Jordan

    Ghassan Kanaan

Authors
  1. Jaffar Atwan
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  2. Masnizah Mohd
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  3. Ghassan Kanaan
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Editor information

Editors and Affiliations

  1. Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Shahrul Azman Noah

  2. Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor D. E, Malaysia

    Azizi Abdullah

  3. Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Haslina Arshad , Zulaiha Ali Othman  & Zalinda Othman ,  & 

  4. School of Computer Science, FTSM, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Azuraliza Abu Bakar

  5. Pattern Recognition Research Group, CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Shahnorbanun Sahran

  6. Faculty of Information Science & IT, National University of Malaysia, 43600, Bangi, Selangor, Malaysia

    Nazlia Omar

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Atwan, J., Mohd, M., Kanaan, G. (2013). Enhanced Arabic Information Retrieval: Light Stemming and Stop Words. In: Noah, S.A., et al. Soft Computing Applications and Intelligent Systems. M-CAIT 2013. Communications in Computer and Information Science, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40567-9_19

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  • DOI: https://doi.org/10.1007/978-3-642-40567-9_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40566-2

  • Online ISBN: 978-3-642-40567-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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