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Support Vector Machine Approach for Examining Arabic Content Reports and Classifying the Part of Speech Tagger

7 Pages Posted: 6 May 2020

See all articles by Maha Ahmed Saidi

Maha Ahmed Saidi

Sohar University

Date Written: February 15, 2020

Abstract

Text classification is the way toward arranging archives into a predefined set of classifications in light of their substance. Arabic is profoundly inflectional and derivational language, which makes content, mining a mind-complicated task. This paper aims to deploy the Support Vector Machines (SVM) for examining Arabic content reports and classifying the Part of speech tagger (POS). This paper reviewed many papers that implemented SVM in tagging words for the Arabic language. The results show that the researcher obtained high accuracy of 99.9% to 88.1%. The results evident that SVM is suitable to deploy the Part of Speech tagging. Also, the more preprocessing task is needed for preparing the text to be ready for the next process phase.

Keywords: Part of Speech ; Arabic text tagging; SVM; NLP ;Machine Learning; Arabic Corpus

Suggested Citation

Saidi, Maha Ahmed, Support Vector Machine Approach for Examining Arabic Content Reports and Classifying the Part of Speech Tagger (February 15, 2020). Available at SSRN: https://ssrn.com/abstract=3573555

Maha Ahmed Saidi (Contact Author)

Sohar University ( email )

P.O Box 44
Al Jameah Street
Sohar, Al Batinah 311
Oman

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