Title :
Neural networks for the automation of Arabic text categorization
Author_Institution :
King Saud Univ., Riyadh, Saudi Arabia
Abstract :
In this paper, we compare and investigate Naive Bayesian Method (NB), K-Nearest Neighbor along with Neural Network method on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorization data sets reveal that NB categorizer outperformed both k-NN and NN algorithms with regard to Fl. Recall and Precision measures.
Keywords :
belief networks; neural nets; text analysis; K-nearest neighbor algorithm; Naïve Bayesian method; arabic text categorization automation; data set; k-NN algorithm; neural network; text evaluation measures; Accuracy; Artificial neural networks; Classification algorithms; Information retrieval; Niobium; Text categorization; Information Retrieval; K-Nearest Neighbor; Machine Learning; Naive Bayesian; Neural Network; Text Categorization;
Conference_Titel :
Computer Applications Technology (ICCAT), 2013 International Conference on
Conference_Location :
Sousse
Print_ISBN :
978-1-4673-5284-0
DOI :
10.1109/ICCAT.2013.6522022
