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A Fuzzy Radial Basis Model for Arabic Documents Classification

  • Taher ZakiEmail author
  • Mohamed Salim El Bazzi
  • Driss Mammass
  • Abdellatif Ennaji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

In this paper, we bring an improvement to the classical fuzzy model of classification by implementing a new approach which based on radial basis functions for the Arabic documents classification. This approach takes into account the concept of semantic vicinity by calculating of the similarity degree between terms in relation to the documents. We combine the calculation of the relevance of these terms (using NEAR operator) with a radial basis function to identify the relevant documents to the query. The use of linguistic resources namely semantic graphs and semantic dictionaries (specifically created for the studied domain) significantly improves the process of classification.

Preliminary and promising results are shown on a Arabic press database which show very good performance compared to the literature.

Keywords

Arabic language Classical fuzzy model Fuzzy classification Radial basis function Relevance Semantic dictionary Semantic graph Semantic vicinity Similarity 

References

  1. 1.
    Aljlayl, M., Frieder, O.: On Arabic search: improving the retrieval effectiveness via a light stemming approach. In: 11th International Conference on Information and Knowledge Management (CIKM), pp. 340–347 (2002)Google Scholar
  2. 2.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction à l’algorithmique, deuxième édition edn. MIT Press, McGraw-Hill, Cambridge, New York City (2001)Google Scholar
  3. 3.
    Daimi, K.: Identifying syntactic ambiguities in single-parse Arabic sentence. Comput. Humanit. 35, 333–349 (2001)CrossRefGoogle Scholar
  4. 4.
    El Bazzi, M.S., Mammass, D., Ennaji, A., Zaki, T.: Features based approach for indexation and representation of unstructured Arabic documents. Adv. Sci. Technol. Eng. Syst. J. 2(3), 900–905 (2017)CrossRefGoogle Scholar
  5. 5.
    El BazzI, M.S., Mammass, D., Zaki, T., Ennaji, A.: A graph-based ranking model for automatic keyphrases extraction from Arabic documents. Advances in Data Mining. Applications and Theoretical Aspects. LNCS (LNAI), vol. 10357, pp. 313–322. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-62701-4_25CrossRefGoogle Scholar
  6. 6.
    El Bazzi, M.S., Zaki, T., Mammass, D., Ennaji, A.: Indexation automatique des textes arabes: état de l’art. E-Ti: Electron. J. Inf. Technol. 41, 48–64 (2016)Google Scholar
  7. 7.
    El Bazzi, M.S., Zaki, T., Mammass, D., Ennaji, A.: Stemming versus multi-words indexing for Arabic documents classification. In: 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA), pp. 1–5 (2016)Google Scholar
  8. 8.
    Khoja, S., Garside, S.: Stemming Arabic Text (1999). http://www.comp.lancs.ac.uk/computing/users/khoja/stemmer.ps
  9. 9.
    Larkey, M., Ballesteros, S.L., Connell, L.: Improving stemming for arabic information retrieval: light stemming and cooccurrence analysis. In: Proceedings of the 25st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2002, pp. 275–282. ACM, New York (2002)Google Scholar
  10. 10.
    Mercier, A., Beigbeder, M.: Application de la logique floue à un modèle de recherche d’information basé sur la proximité. In: Dans les Actes LFA 2004, pp. 231–237 (2005)Google Scholar
  11. 11.
    Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41(2), 10:1–10:69 (2009)CrossRefGoogle Scholar
  12. 12.
    Quillian, R.M.: Semantic memory. In: Semantic Information Processing, pp. 216–270. MIT Press, Cambridge (1968)Google Scholar
  13. 13.
    Rada, R., Mili, H., Bicknell, E.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19, 17–30 (1989)CrossRefGoogle Scholar
  14. 14.
    Tchechmedjiev, A.: État de l’art sur les mesures de similarité sémantique locales et algorithmes globaux pour la désambiguïsation lexicale à base de connaissances. In: Actes de la conférence conjointe JEP-TALN-RECITAL. RECITAL 2012, vol. 3, pp. 295–308 (2012)Google Scholar
  15. 15.
    Wagner, C.: Breaking the knowledge acquisition bottleneck through conversational knowledge management. Inf. Resour. Manag. J. 19(1), 70–83 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Taher Zaki
    • 1
    Email author
  • Mohamed Salim El Bazzi
    • 1
  • Driss Mammass
    • 1
  • Abdellatif Ennaji
    • 2
  1. 1.IRFSIC Laboratory, Faculty of ScienceIbn Zohr UniversityAgadirMorocco
  2. 2.LITIS Laboratory EA 4108, University of RouenRouenFrance

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