Paper Details: Downloads: 488
Serial Number: P1121053414
Title: An Arabic Stemming Approach using Machine Learning with Arabic Dialogue System
Authors: Mohammad Hijjawi and Zuhair Bandar and Keeley Crockett and David Mclean
Abstract: Stemming plays a vital role in text-based searching systems in general and particularly in information retrieval systems. The current Arabic stemming approaches especially those which deal with linguistics still face a number of challenges and they have not been highly accurate yet. This is mostly due to rich derivational and inflectional features in the Arabic language. This paper proposes a novel machine learning based methodology in order to overcome the stemming problem in the Arabic language. Results have shown that this novel methodology achieved a very high level of accuracy. Also, such a stemming approach has been proposed in this paper to be usable in Arabic dialogue systems as a preprocessing stage.
Keywords: Arabic, Stemming, Machine Learning, Decision Tree, Pattern Matching, and Dialogue system.
Journal/Conference: ICGST Conference on Artificial Intelligence and Machine Learning, AIML-11
Volume:
Issue:
Submission Date: 12/30/2010 12:00:00 AM
Review Date: 3/25/2011 12:00:00 AM
Publishing Date: 4/5/2011 12:00:00 AM
Article Downloads: 488
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