An Evolutionary Model for Selecting Relevant Textual Features

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Taher Zaki 1,* Mohamed Salim EL Bazzi 1 Driss Mammass 1

1. IRF-SIC Laboratory, Faculty of Science, Ibn Zohr University, Agadir, Morocco

* Corresponding author.


Received: 20 Aug. 2018 / Revised: 20 Sep. 2018 / Accepted: 17 Oct. 2018 / Published: 8 Nov. 2018

Index Terms

Arabic text, classification, natural selection, semantic vicinity, textual data, keyword extraction


From a philosophical point of view, the words of a text or a speech are not held just for informational purposes, but they act and react; they have the power to react on their counterparts. Each word, evokes similar or different senses that can influence and interact with the following words, it has a vibratory property. It's not the words themselves that have the impact, but the semantic reaction behind the words. In this context, we propose a new textual data classification approach while trying to imitate human altruistic behavior in order to show the semantic altruistic stakes of natural language words through statistical, semantic and distributional analysis. We present the results of a word extraction method, which combines a distributional proximity index, a selection coefficient and a co-occurrence index with respect to the neighborhood.

Cite This Paper

Taher Zaki, Mohamed Salim EL Bazzi, Driss Mammass, " An Evolutionary Model for Selecting Relevant Textual Features", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.11, pp. 43-50, 2018. DOI:10.5815/ijmecs.2018.11.06


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