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Volume 9, Issue 2
A Kernel Hybridization NGram-Okapi for Indexing and Classification of Arabic Documents

Taher Zaki , Driss Mammass , Abdellatif Ennaji and Stéphane Nicolas

J. Info. Comput. Sci. , 9 (2014), pp. 141-153.

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  • Abstract
In this paper, we propose a hybrid system for contextual and semantic indexing of Arabic documents, bringing an improvement to classical models based on n-grams and the Okapi model. This new approach takes into account the concept of the semantic vicinity of terms. We proceed in fact by the calculation of similarity between words using an hybridization of NGRAMs-OKAPI statistical measures and a kernel function in order to identify relevant descriptors. Terminological resources such as graphs and semantic dictionaries are integrated into the system to improve the indexing and the classification processes.
  • Keywords

Arabic documents, classification, indexing, kernel function, n-grams, okapi.

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COPYRIGHT: © Global Science Press

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@Article{JICS-9-141, author = {Taher Zaki , Driss Mammass , Abdellatif Ennaji and Stéphane Nicolas}, title = {A Kernel Hybridization NGram-Okapi for Indexing and Classification of Arabic Documents}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {9}, number = {2}, pages = {141--153}, abstract = { In this paper, we propose a hybrid system for contextual and semantic indexing of Arabic documents, bringing an improvement to classical models based on n-grams and the Okapi model. This new approach takes into account the concept of the semantic vicinity of terms. We proceed in fact by the calculation of similarity between words using an hybridization of NGRAMs-OKAPI statistical measures and a kernel function in order to identify relevant descriptors. Terminological resources such as graphs and semantic dictionaries are integrated into the system to improve the indexing and the classification processes. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22591.html} }
TY - JOUR T1 - A Kernel Hybridization NGram-Okapi for Indexing and Classification of Arabic Documents AU - Taher Zaki , Driss Mammass , Abdellatif Ennaji and Stéphane Nicolas JO - Journal of Information and Computing Science VL - 2 SP - 141 EP - 153 PY - 2024 DA - 2024/01 SN - 9 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22591.html KW - Arabic documents, classification, indexing, kernel function, n-grams, okapi. AB - In this paper, we propose a hybrid system for contextual and semantic indexing of Arabic documents, bringing an improvement to classical models based on n-grams and the Okapi model. This new approach takes into account the concept of the semantic vicinity of terms. We proceed in fact by the calculation of similarity between words using an hybridization of NGRAMs-OKAPI statistical measures and a kernel function in order to identify relevant descriptors. Terminological resources such as graphs and semantic dictionaries are integrated into the system to improve the indexing and the classification processes.
Taher Zaki , Driss Mammass , Abdellatif Ennaji and Stéphane Nicolas. (2024). A Kernel Hybridization NGram-Okapi for Indexing and Classification of Arabic Documents. Journal of Information and Computing Science. 9 (2). 141-153. doi:
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