Feature reduction techniques for Arabic text categorization

Duwairi, Rehab; Al-Refai, Mohammad Nayef; Khasawneh, Natheer

doi:10.1002/asi.21173

Cited by 52 publications

(32 citation statements)

References 11 publications

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“…He claimed that experiments proved that the stemming technique is not always effective for Arabic document categorization. His experiments [4] ' ' ' ' English SVM Toman et al [5] ' ' ' -English and Czech NB Chirawichitchai et al [9] -' ' ' Thai NB, DT, SVM Mesleh [10,11] ' ' -' Arabic SVM Duwairi et al [15] -' ' -Arabic KNN Kanan [14] -' ' -Arabic SVM, NB, RF Zaki et al [18] ' ' ' -Arabic KNN Al-Shargabi et al [12] -' --Arabic NB, SVM, J48 Khorsheed et al [16] -' -' Arabic KNN, NB, SVM, etc. Ababneh et al [17] ' ' --Arabic KNN…”

Section: Related Workmentioning

confidence: 99%

“…Uysal et al [7] N/F -+ (often) Pomikálek et al [6] N/F + + Méndez et al [8] N/F --Chirawichitchai et al [9] N/F N/F N/F Song et al [4] N/F + + Toman et al [5] N/F + -Mesleh [10,11] N/F N/F -Duwairi et al [15] N/F N/F + Kanan [14] 2015 N/F N/F + Zaki et al [18] N/F N/F + Al-Shargabi et al [12] N/F + N/F Khorsheed et al [16] N/F N/F N/F Ababneh et al [17] N/F N/F -…”

Section: Nr Sr Lsmentioning

confidence: 99%

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The Effect of Preprocessing on Arabic Document Categorization

et al. 2016

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Abstract:Preprocessing is one of the main components in a conventional document categorization (DC) framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM). Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Nr Sr Lsmentioning

confidence: 99%

The Effect of Preprocessing on Arabic Document Categorization

et al. 2016

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“…This method does not significantly improve TC results over the BoW method (Elberrichi and Abidi, 2012). Extracting the root of the word using stemming methods has also been used to enhance TC results (Duwairi et al, 2009;Kanaan et al, 2009;Syiam et al, 2006). Still other researchers have attempted to use words in their orthographic form (without stemming) in TC (Mesleh, 2007;Thabtah et al, 2009).…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Yousif¹,

Samawi²,

Elkabani³

et al. 2015

Journal of Computer Science

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Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information (written in Arabic language) on the web. In most text classification processes, feature selection is crucial task since it highly affects the classification accuracy. Generally, two types of features could be used: Statistical based features and semantic and concept features. The main interest of this paper is to specify the most effective semantic and concept features on Arabic text classification process. In this study, two novel features that use lexical, semantic and lexico-semantic relations of Arabic WordNet (AWN) ontology are suggested. The first feature set is List of Pertinent Synsets (LoPS), which is list of synsets that have a specific relation with the original terms. The second feature set is List of Pertinent Words (LoPW), which is list of words that have a specific relation with the original terms. Fifteen different relations (defined in AWN ontology) are used with both proposed features. Naïve Bayes classifier is used to perform the classification process. The experimental results, which are conducted on BBC Arabic dataset, show that using LoPS feature set improves the accuracy of Arabic text classification compared with the well-known Bag-of-Word feature and the recent Bag-of-Concept (synset) features. Also, it was found that LoPW (especially with related-to relation) improves the classification accuracy compared with LoPS, Bagof-Word and Bag-of-Concept.

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“…al [18], compared three dimensionality reduction techniques; stemming, light stemming, and word cluster. The purpose of employing the previous methods is to reduce the size of documents vectors without affecting the accuracy of the classifiers.…”

Section: Related Workmentioning

confidence: 99%

Design and Evaluation of a Parallel Classifier for Large-Scale Arabic Text

AbuTair¹,

Baraka²

2013

IJCA

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Text classification has become one of the most important techniques in text mining. A number of machine learning algorithms have been introduced to deal with automatic text classification. One of the common classification algorithms is the k-NN algorithm which is known to be one of the best classifiers applied for different languages including Arabic language. However, the k-NN algorithm is of low efficiency because it requires a large amount of computational power. Such a drawback makes it unsuitable to handle a large volume of text documents with high dimensionality and in particular in the Arabic language. This paper introduces a high performance parallel classifier for large-scale Arabic text that achieves the enhanced level of speedup, scalability, and accuracy. The parallel classifier is based on the sequential k-NN algorithm. The classifier has been tested using the OSAC corpus. The performance of the parallel classifier has been studied on a multicomputer cluster. The results indicate that the parallel classifier has very good speedup and scalability and is capable of handling large documents collections with higher classification results.

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Feature reduction techniques for Arabic text categorization

Cited by 52 publications

References 11 publications

The Effect of Preprocessing on Arabic Document Categorization

The Effect of Preprocessing on Arabic Document Categorization

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Design and Evaluation of a Parallel Classifier for Large-Scale Arabic Text

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