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Machine Learning Implementations in Arabic Text Classification

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Intelligent Natural Language Processing: Trends and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 740))

Abstract

Text categorization denotes the process of assigning to a piece of text a label that describes its thematic information. Although this task has been extensively investigated for different languages, it has not been researched thoroughly with respect to the Arabic language. In this chapter, we summarize the major techniques used for addressing different aspects of the text classification problem. These aspects include problem formalization using vector space model, term weighting, feature reduction, and classification algorithms. We pay special attention to the part of research devoted to text categorization in the Arabic language. We conclude that the effect of language is minimized with respect to this task. Moreover, we list the currently unsolved issues in the text classification context and thereby highlight the active research directions.

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Notes

  1. 1.

    BOOSTEXTER is available from http://www.cs.princeton.edu/˜schapire/boostexter.html.

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Author information

Authors and Affiliations

  1. Computer Science Department, Cairo University, Giza, Egypt

    Mohammed Elarnaoty

  2. Baaz, Inc, San Francisco, USA

    Ali Farghaly

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  1. Mohammed Elarnaoty
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  2. Ali Farghaly
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Correspondence to Ali Farghaly .

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Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  2. Faculty of Computers and Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Faculty of Computers and Information, Ain Shams University, Cairo, Egypt

    Fahmy Tolba

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Elarnaoty, M., Farghaly, A. (2018). Machine Learning Implementations in Arabic Text Classification. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-67056-0_15

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