Title :
Arabic text categorization using SVM active learning technique: An overview
Author :
Goudjil, Mohamed ; Koudil, Mouloud ; Hammami, N. ; Bedda, M. ; Alruily, Meshrif
Author_Institution :
Fac. of Comput. Sci. & Inf., Al Jouf Univ., Sakaka, Saudi Arabia
Abstract :
Support vector machine is one of the famous techniques used in active learning to reduce the data labeling effort in different fields of pattern recognition. Most of the studies on applying active learning methods to automatic text classification focused on requesting the label of a single unlabeled document in each iteration. In this paper, we present a novel batch mode active learning using SVM for Arabic text classification.
Keywords :
learning (artificial intelligence); natural language processing; pattern classification; support vector machines; text analysis; Arabic text categorization; Arabic text classification; SVM active learning; automatic text classification; batch mode active learning; data labeling; pattern recognition; single unlabeled document; support vector machine; Classification algorithms; Educational institutions; Labeling; Learning systems; Support vector machines; Text categorization; Training; Active learning; Arabic text classification; Batch-mode active learning; pool-based active learning; support vector machine (SVM);
Conference_Titel :
Computer and Information Technology (WCCIT), 2013 World Congress on
Conference_Location :
Sousse
Print_ISBN :
978-1-4799-0460-0
DOI :
10.1109/WCCIT.2013.6618666
