چکیده (انگلیسی):

Arabic text classification methods have emerged as a natural
result of the existence of a massive amount of varied textual information
(written in Arabic language) on the web. In most text classification
processes, feature selection is crucial task since it highly affects the
classification accuracy. Generally, two types of features could be used:
Statistical based features and semantic and concept features. The main
interest of this paper is to specify the most effective semantic and concept
features on Arabic text classification process. In this study, two novel
features that use lexical, semantic and lexico-semantic relations of Arabic
WordNet (AWN) ontology are suggested. The first feature set is List of
Pertinent Synsets (LoPS), which is list of synsets that have a specific
relation with the original terms. The second feature set is List of Pertinent
Words (LoPW), which is list of words that have a specific relation with
the original terms. Fifteen different relations (defined in AWN ontology)
are used with both proposed features. Naïve Bayes classifier is used to
perform the classification process. The experimental results, which are
conducted on BBC Arabic dataset, show that using LoPS feature set
improves the accuracy of Arabic text classification compared with the
well-known Bag-of-Word feature and the recent Bag-of-Concept (synset)
features. Also, it was found that LoPW (especially with related-to
relation) improves the classification accuracy compared with LoPS, Bagof-
Word and Bag-of-Concept.