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An Intelligent System Based on Statistical Learning For Searching in Arabic Text Reda A. El-Khoribi and Mahmoud A. Ismael Faculty of Computers and Information, Cairo University, Giza, Egypt In this paper, a novel Arabic text categorization system has been developed based on statistical learning. The system uses a new method for feature extraction. The system has been implemented and tested using an Arabic text corpus. Results prove that the efficiency of the proposed system in text categorization of Arabic documents. Moreover, the system proved powerfulness in grasping the semantics of documents so that it has encouragement results as a question answering system. Keywords: Text mining, Text Categorization, Hidden Markov Models, Arabic Stemming
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Biography:
BibTex: @ARTICLE{P1120634001, AUTHOR = {Reda A. El-Khoribi and Mahmoud A. Ismael}, TITLE = {An Intelligent System Based on Statistical Learning For Searching in Arabic Text}, JOURNAL ={ICGST International Journal on Artificial Intelligence and Machine Learning, AIML},
YEAR = {2006},
VOLUME = {6}, ISSUE ={3}, PAGES = {41--47} } ( |
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