A supervised approach for multi-label classification of Arabic news articles
Mohammed A. Shehab, Omar Badarneh, Mahmoud Al‐Ayyoub, Yaser Jararweh · 2016
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.