M. Shehab, Omar Badarneh, M. Al-Ayyoub, Y. Jararweh
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A supervised approach for multi-label classification of Arabic news articles
Multi-label classification of textual data is an important problem with the growing size of available data and the increasing difficulties in assigning a single label to each piece of text. Examples range from news articles to emails. Most of the existing works consider English text. This work focuses on multi-label classification of Arabic articles. After dataset collection, three multi-label classifiers are considered (DT, RF and KNN). The results show a superiority of DT over the other two classifiers.