A bi-technical analysis for Arabic stop-words detection

Authors

  • Namly D Mohammadia School of Engineers, Mohammed Vth University - Rabat, Morocco
  • Bouzoubaa K Mohammadia School of Engineers, Mohammed Vth University - Rabat, Morocco
  • Yousfi A Faculty of Legal, Economic and Social Sciences-Souissi, Mohammed Vth University-Rabat, Morocco

Keywords:

NLP, Stop-words, Supervised machine learning, Arabic, Information retrieval

Abstract

Stop words are defined as words that frequently appear in texts without carrying any significant information. For the Arabic language, existing works suffer from two main drawbacks (i) the use of only proprietary corpus and (ii) the reliance of only the frequency metric. Our approach for automatic Arabic stop-words detection uses a new metric based on a supervised machine learning process and a vector space representation that can be applied to any corpus, taking into account both domain independent and domain-dependent stop-words. Conducted experiments to evaluate the proposed approach show a significant improvement reaching 91.85% for the detection rate using the F-measure metric.

References

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Published

2024-02-26

How to Cite

Namly, D., Bouzoubaa, K., & Yousfi, A. (2024). A bi-technical analysis for Arabic stop-words detection. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(05), 3126–3134. Retrieved from https://ijact.in/index.php/j/article/view/491

Issue

Vol. 8 No. 05 (2019): May

Section

Original Research Article

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Copyright (c) 2019 COMPUSOFT: An International Journal of Advanced Computer Technology

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