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A Survey and Comparative Study of Arabic NLP Architectures

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Intelligent Natural Language Processing: Trends and Applications

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

Abstract

Arabic Natural Language Processing (ANLP) has known a significant progress during the last years. As a result, several ANLP tools and applications have been developed such as tokenizers, Part Of Speech taggers, morphological analyzers, syntactic parsers, etc. However, most of these tools are heterogeneous and can hardly be reused in the context of other projects without modifying their source code. This problem is known to be common to all languages, that is why some advanced NLP language independent architectures have emerged such as GATE (Cunningham et al. ACL, 2002) [1] and UIMA (Apache UIMA Manuals and Guides, 2015) [2]. These architectures have significantly changed the way NLP applications are designed and developed. They provide homogenous structures for applications, better reusability and faster deployment. In this article, we present a comparative study of NLP architectures in order to specify which ones can suitably deal with Arabic language and its specificities.

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Notes

  1. 1.

    https://www.britishcouncil.org/voices-magazine/surprising-facts-about-arabic-language.

  2. 2.

    http://www.lrec-conf.org/lrec2010/.

  3. 3.

    https://uima.apache.org/.

  4. 4.

    http://www-01.ibm.com/software/ecm/content-analytics/uima.html.

  5. 5.

    https://gate.ac.uk/.

  6. 6.

    https://opennlp.apache.org/.

  7. 7.

    http://www.nooj-association.org/.

  8. 8.

    http://www.nltk.org.

  9. 9.

    https://www.gutenberg.org/.

  10. 10.

    http://www.alias-i.com/lingpipe/.

  11. 11.

    http://alias-i.com/.

  12. 12.

    https://github.com/aymara/lima.

  13. 13.

    http://www.kalisteo.eu/en/index.htm.

  14. 14.

    https://www.microsoft.com/en-us/research/project/arabic-toolkit-service-atks/.

  15. 15.

    http://innovation.columbia.edu/technologies/cu14012_arabic-language-disambiguation-for-natural-language-processing-applications.

  16. 16.

    https://sites.google.com/site/mahajalthobaiti/resources.

  17. 17.

    http://arabic.emi.ac.ma/safar/.

  18. 18.

    http://arabic.emi.ac.ma:8080/SafarWeb_V2/.

  19. 19.

    http://searchservervirtualization.techtarget.com/definition/platform.

  20. 20.

    http://whatis.techtarget.com/definition/framework.

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Acknowledgements

We would like to thank Professor Mohamed Issam Kabbaj (Mohammadia School of Engineers, Mohammed Vth University in Rabat, Morocco) for his feedback on the work presented in Sect. 4.2.

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Authors and Affiliations

  1. Mohammadia School of Engineers, Mohammed Vth University in Rabat, Rabat, Morocco

    Younes Jaafar & Karim Bouzoubaa

Authors
  1. Younes Jaafar
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  2. Karim Bouzoubaa
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Correspondence to Younes Jaafar .

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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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Jaafar, Y., Bouzoubaa, K. (2018). A Survey and Comparative Study of Arabic NLP Architectures. 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_28

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  • DOI: https://doi.org/10.1007/978-3-319-67056-0_28

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