A Study of the Effects of Stemming Strategies on Arabic Document Classification
Stemming is one of the most effective techniques, which has been adopted in many applications, such as machine learning, machine translation, document classification (DC), information retrieval, and natural language processing. The stemming technique is meant to be applied during the classification...
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doaj-8f5b5fcc48d7472c85b88b503f66e0192021-03-29T22:50:45ZengIEEEIEEE Access2169-35362019-01-017326643267110.1109/ACCESS.2019.29033318664087A Study of the Effects of Stemming Strategies on Arabic Document ClassificationYousif A. Alhaj0https://orcid.org/0000-0002-8770-1584Jianwen Xiang1Dongdong Zhao2https://orcid.org/0000-0002-4697-6901Mohammed A. A. Al-Qaness3https://orcid.org/0000-0002-6956-7641Mohamed Abd Elaziz4https://orcid.org/0000-0002-7682-6269Abdelghani Dahou5Hubei Key Laboratory of Transportation of Internet of Things, School of Computer Science and Technology, Wuhan University of Technology, Wuhan, ChinaHubei Key Laboratory of Transportation of Internet of Things, School of Computer Science and Technology, Wuhan University of Technology, Wuhan, ChinaHubei Key Laboratory of Transportation of Internet of Things, School of Computer Science and Technology, Wuhan University of Technology, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig, EgyptHubei Key Laboratory of Transportation of Internet of Things, School of Computer Science and Technology, Wuhan University of Technology, Wuhan, ChinaStemming is one of the most effective techniques, which has been adopted in many applications, such as machine learning, machine translation, document classification (DC), information retrieval, and natural language processing. The stemming technique is meant to be applied during the classification of documents to reduce the high dimensionality of the feature space, which, in turn, raises the functioning of the classification system, particularly with extreme modulated language, for instance, Arabic language. This paper aims to study the impact of stemming techniques, namely Information Science Research Institute (ISRI), Tashaphyne, and ARLStem on Arabic DC. The classification algorithms, namely Naïve Bayesian (NB), support vector machine (SVM), and K-nearest neighbors (KNN), are used in this paper. In addition, the chi-square feature selection is used to select the most relevant features. Experiments are conducted on CNN Arabic corpus, which is collected from Arabic websites to assess the performance of the classification system. In order to evaluate the classifiers, the K-fold cross-validation method and Micro-F1 are used. Findings of this paper indicate that the ARLStem outperforms the ISRI and Tashaphyne stemmers. The outcomes clearly showed the effectiveness of the SVM over the KNN and NB classifiers, which achieved 94.64% Micro-F1 value when using the ARLStem stemmer.https://ieeexplore.ieee.org/document/8664087/Arabic text classificationtext preprocessingstemming techniquesfeature extractionfeature selection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yousif A. Alhaj Jianwen Xiang Dongdong Zhao Mohammed A. A. Al-Qaness Mohamed Abd Elaziz Abdelghani Dahou |
spellingShingle |
Yousif A. Alhaj Jianwen Xiang Dongdong Zhao Mohammed A. A. Al-Qaness Mohamed Abd Elaziz Abdelghani Dahou A Study of the Effects of Stemming Strategies on Arabic Document Classification IEEE Access Arabic text classification text preprocessing stemming techniques feature extraction feature selection |
author_facet |
Yousif A. Alhaj Jianwen Xiang Dongdong Zhao Mohammed A. A. Al-Qaness Mohamed Abd Elaziz Abdelghani Dahou |
author_sort |
Yousif A. Alhaj |
title |
A Study of the Effects of Stemming Strategies on Arabic Document Classification |
title_short |
A Study of the Effects of Stemming Strategies on Arabic Document Classification |
title_full |
A Study of the Effects of Stemming Strategies on Arabic Document Classification |
title_fullStr |
A Study of the Effects of Stemming Strategies on Arabic Document Classification |
title_full_unstemmed |
A Study of the Effects of Stemming Strategies on Arabic Document Classification |
title_sort |
study of the effects of stemming strategies on arabic document classification |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Stemming is one of the most effective techniques, which has been adopted in many applications, such as machine learning, machine translation, document classification (DC), information retrieval, and natural language processing. The stemming technique is meant to be applied during the classification of documents to reduce the high dimensionality of the feature space, which, in turn, raises the functioning of the classification system, particularly with extreme modulated language, for instance, Arabic language. This paper aims to study the impact of stemming techniques, namely Information Science Research Institute (ISRI), Tashaphyne, and ARLStem on Arabic DC. The classification algorithms, namely Naïve Bayesian (NB), support vector machine (SVM), and K-nearest neighbors (KNN), are used in this paper. In addition, the chi-square feature selection is used to select the most relevant features. Experiments are conducted on CNN Arabic corpus, which is collected from Arabic websites to assess the performance of the classification system. In order to evaluate the classifiers, the K-fold cross-validation method and Micro-F1 are used. Findings of this paper indicate that the ARLStem outperforms the ISRI and Tashaphyne stemmers. The outcomes clearly showed the effectiveness of the SVM over the KNN and NB classifiers, which achieved 94.64% Micro-F1 value when using the ARLStem stemmer. |
topic |
Arabic text classification text preprocessing stemming techniques feature extraction feature selection |
url |
https://ieeexplore.ieee.org/document/8664087/ |
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