¹Laboratory IRF-SIC, University Ibn Zohr, Agadir, Morocco.
²Laboratory LITIS, University of Rouen, Rouen, France.

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Abstract—Text indexing aims to take the full advantage of textual data to help intelligent programs to make relevant decisions. In order to explore a large amount of textual documents, and to disclose semantic information hidden in unstructured documents, like texts, an effective indexation system is required. In this paper, we propose a new approach for indexing Arabic texts. Based on the semantic proximity and taking into account the contexts contained in each document, our method is denoted contextual indexing. Several algorithms are used for keywords extraction, each of them emphasizes some criterion. However, we target the most descriptive keywords for each document. We also propose a new approach for document modeling. We compared the results obtained using our method with those obtained by an indexation system based on a standard statistical method. The experimental results demonstrate the performance of our approach.

Index Terms—Contextual indexation, semantic proximity, clustering, Arabic documents.

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Cite: Mohamed Salim El Bazzi, Driss Mammass, Abdelatif Ennaji, Taher Zaki, "Toward a Complex System for Context Discovery to Index Arabic Documents," Journal of Computers vol. 13, no. 8, pp. 955-962, 2018.