Our results show that the use of single words as a feature provides greater classification accuracy (CA) for ATC compared to N-grams. Moreover, CA decreases by ...

Character N-grams, word roots, word stems, and full words have been altogether used as features for Arabic text classification. No prior studies, as shown ...

For classifying Arabic text sources the N-Gram Frequency Statistics technique is investigated by Khreisat, (2006) . This method is based on both Dice similarity ...

This paper presented the results of classifying Arabic text documents using the tri-gram frequency statistics technique employing a dissimilarity measure.

Apr 5, 2015 · n-grams work by capturing this structure. Thus, certain combinations of letters are more likely in some languages than others. This is the basis of n-gram ...

This paper proposes a new multilingual stemmer based on the extraction of word root and in which the technique of n-grams is used and validated on three ...

Results show that, compared to stem or word N-grams, the use of root 1-grams as a feature provides greater classification performance for Arabic text ...

Using Word N-Grams as Features in Arabic Text Classification. https://doi.org ... Khreisat, L.: A machine learning approach for Arabic text classification using N ...

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N-gram character and the word N-gram are used as feature terms. The performance for FRAM outperforms the Naive Bayes method (baseline method).The technique is ...