Stemming Effectiveness in Clustering of Arabic Documents

Ghanem, Osama A.; Ashour, Wesam M.

doi:10.5120/7620-0674

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…In a study presented in Ghanem and Ashour [44], the effect of using full-word, stem, and root forms on clustering of Arabic documents was discussed. The comparison was based on different measures of precision, recall, and F-measure.…”

Section: Related Workmentioning

confidence: 99%

The impact of indexing approaches on Arabic text classification

Al-Badarneh

Al-Shawakfa

Bani-Ismail

et al. 2016

Journal of Information Science

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This paper investigates the impact of using different indexing approaches (full-word, stem, and root) when classifying Arabic text. In this study, the naïve Bayes classifier is used to construct the multinomial classification models and is evaluated using stratified k-fold cross-validation ( k ranges from 2 to 10). It is also uses a corpus that consists of 1000 normalized Arabic documents. The results of one experiment in this study show that significant accuracy improvements have occurred when the full-word form is used in most k-folds. Further experiments show that the classifier has achieved the highest accuracy in the eight-fold by using 7/8–1/8 train–test ratio, despite the indexing approach being used. The overall results of this study show that the classifier has achieved the maximum micro-average accuracy 99.36%, either by using the full-word form or the stem form. This proves that the stem is a better choice to use when classifying Arabic text, because it makes the corpus dataset smaller and this will enhance both the processing time and storage utilization, and achieve the highest level of accuracy.

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Section: Related Workmentioning

confidence: 99%

The impact of indexing approaches on Arabic text classification

Al-Badarneh

Al-Shawakfa

Bani-Ismail

et al. 2016

Journal of Information Science

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“…In the case of the Arabic language, stemming has been the subject of several studies that have shown its effectiveness in both text classification and clustering [4], [6], [19], [20]. However, three recent comparative studies investigated different Arabic stemmers in the field of Arabic text classification.…”

Section: Stemmingmentioning

confidence: 99%

The effects of Pre-Processing Techniques on Arabic Text Classification

2021

IJATCSE

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In the last two decades, the amount of available Arabic text data on the World Wide Web is dramatically growing, making it the fourth most used language on the web. Accordingly, the demand for efficient Arabic text classification is increasing, especially for web page content filtering, information retrieval, and e-mail spam detection. Several Machine Learning algorithms have been implemented to classify Arabic documents. However, the results achieved are not comparable with those obtained in other languages such as English, primarily when using preprocessing techniques that do not take into consideration the Arabic language features. This paper investigates the impact of wisely selected preprocessing techniques on the efficiency of different text classification algorithms. The effects of stop words removal, stemming, lemmatization, and all possible combinations are examined. The reported results (+10.75% to +28.73%) prove the effectiveness of using these techniques either individually or in combination.

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“…Ghanem and Ashour [13] established that clustering plays a fundamental role in achieving good results during IR. The use of stemming techniques enables the reduction of redundant features that have similar roots in root‐based stemming and ones that exhibit the same syntactical form.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

2021

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The internet has become one of the main sources of news spread as it unleashed the information dissemination space, where the news websites express opinions on entities while also reporting on recent or unusual security risks. Recently, many research studies have focused on sentimental reflection on the views and impressions of people utilising natural language processing and analytical linguistics. Therefore, we have collected corpus from popular Arabic websites that publish articles related to recent security issues, and we provide light weight preprocessing techniques where data is term matrix is transformed. We also present an intensive lexical-driven data analysis with visualised data views, as our topic modelling technique can effectively extract significant topics from all the collected text from different websites. Our experiments validate the k-means clustering algorithm with and without the latent Dirichlet allocation topic modelling method, and we adopted various validation techniques to measure the topic clustering internally and externally. As shown in the experiments' results, our proposed combined method has a high round index rate of 87.2%, with a large number of topics and clusters. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Stemming Effectiveness in Clustering of Arabic Documents

Cited by 10 publications

References 9 publications

The impact of indexing approaches on Arabic text classification

The impact of indexing approaches on Arabic text classification

The effects of Pre-Processing Techniques on Arabic Text Classification

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

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