Arabic text classification: Review study

An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defin...

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Main Authors:	Hijazi, Musab Mustafa, Zeki, Akram M., Ismail, Amelia Ritahani
Format:	Article
Language:	English
Published:	Medwell Publishing 2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/51845/ http://irep.iium.edu.my/51845/ http://irep.iium.edu.my/51845/1/Arabic%20Text%20Classification.pdf

id	iium-51845
recordtype	eprints
spelling	iium-518452017-03-21T09:38:36Z http://irep.iium.edu.my/51845/ Arabic text classification: Review study Hijazi, Musab Mustafa Zeki, Akram M. Ismail, Amelia Ritahani QA75 Electronic computers. Computer science An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia Medwell Publishing 2016 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51845/1/Arabic%20Text%20Classification.pdf Hijazi, Musab Mustafa and Zeki, Akram M. and Ismail, Amelia Ritahani (2016) Arabic text classification: Review study. Journal of Engineering and Applied Science, 11 (3). pp. 528-536. ISSN 1816-949X E-ISSN 1818-7803 http://docsdrive.com/pdfs/medwelljournals/jeasci/2016/528-536.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Hijazi, Musab Mustafa Zeki, Akram M. Ismail, Amelia Ritahani Arabic text classification: Review study
description	An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia
format	Article
author	Hijazi, Musab Mustafa Zeki, Akram M. Ismail, Amelia Ritahani
author_facet	Hijazi, Musab Mustafa Zeki, Akram M. Ismail, Amelia Ritahani
author_sort	Hijazi, Musab Mustafa
title	Arabic text classification: Review study
title_short	Arabic text classification: Review study
title_full	Arabic text classification: Review study
title_fullStr	Arabic text classification: Review study
title_full_unstemmed	Arabic text classification: Review study
title_sort	arabic text classification: review study
publisher	Medwell Publishing
publishDate	2016
url	http://irep.iium.edu.my/51845/ http://irep.iium.edu.my/51845/ http://irep.iium.edu.my/51845/1/Arabic%20Text%20Classification.pdf
first_indexed	2023-09-18T21:13:30Z
last_indexed	2023-09-18T21:13:30Z
_version_	1777411409536090112

Arabic text classification: Review study

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