An efficient stemming for Arabic Text Classification

Nehar, Attia; Ziadi, Djelloul; Cherroun, Hadda; Guellouma, Younes

doi:10.1109/innovations.2012.6207760

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…CAD includes predominantly spoken vernaculars, and each Arab country has its dialect. MSA is the official language and includes news, media, and official documents [3]. The direction of writing in the Arabic language is from right to left.…”

Section: Overview Of Arabic Language Structurementioning

confidence: 99%

“…In doing so, the effect of preprocessing task can be comparatively observed within a wide range of feature size. Feature vectors were built using the function TFiDF as described by Equations (2) and (3).…”

Section: Experimental Configuration and Performance Measurementioning

confidence: 99%

“…Currently, DC is widely used in different domains, such as mail spam filtering, article indexing, Web searching, and Web page categorization [3]. These applications are increasingly becoming important in the information-oriented society at present.…”

Section: Introductionmentioning

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: Overview Of Arabic Language Structurementioning

confidence: 99%

Section: Experimental Configuration and Performance Measurementioning

confidence: 99%

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The Effect of Preprocessing on Arabic Document Categorization

et al. 2016

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“…Arabic language consists of three categories: verb, noun, and particle [4] and has three forms; historical liturgical text and old literature texts called Classical Arabic (CA), news media and formal speech called Modern Standard Arabic (MSA), and Dialectal Arabic (DA) which includes predominantly spoken vernaculars and has no written standards, Arabic alphabet consists of 28 basic letters and the Hamza, three of these letters are vowels and the rest are consonants, like all Semitic languages, Arabic orientation is from right to left, it differs from other languages syntactically, morphologically and semantically [11].…”

Section: Arabic Language Characteristicsmentioning

confidence: 99%

Arabic stemming techniques: Comparisons and new vision

Al-Zyoud

Al-Rabayah

2015

2015 IEEE 8th GCC Conference &Amp; Exhibition

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Arabic information extraction processes have become a popular area of research. Many methods and approaches have designed and introduced algorithms to solve the problem of morphology and stemming of Arabic language. Each researcher proposed his own standards, testing methodology and accuracy measurements to test his algorithm. Therefore, we cannot make an exact comparison between these algorithms. However, this research goes over stemming processes by explaining and discussing Arabic language characteristics and difficulties of stemming it, comparing root-based stemming, suffix and prefix -based stemming, and translation base stemming against each other, representing a modified stemming algorithm which helps go over some missing words in other algorithms. And finally, representing a new vision of Arabic stemming techniques.

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“…However, the task of performing the retrieve of information of Arabic language is very problematic, because of many aspects, such as: polysemy, irregular and inflected derived forms, various spelling of certain words, various writing of certain combination character, short vowels (diacritics) and long vowels, and the spectacular availability of affixes in the Arabic words [1,2]. Different methods and approaches have been introduced to retrieve Arabic information [2,3,4,5,6].…”

Section: Introduction and Related Workmentioning

confidence: 99%