Vector Space Models to Classify Arabic Text

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2014 by IJCTT Journal
Volume-7 Number-4                          
Year of Publication : 2014
Authors : Jafar Ababneh, Omar Almomani, Wael Hadi, Nidhal Kamel Taha El-Omari, Ali Al-Ibrahim
DOI :  10.14445/22312803/IJCTT-V7P109

citation

      Jafar Ababneh, Omar Almomani, Wael Hadi, Nidhal Kamel Taha El-Omari, Ali Al-Ibrahim. Article: Vector Space Models to Classify Arabic Text. International Journal of Computer Trends and Technology (IJCTT) 7(4):219-223, January 2014. Published by Seventh Sense Research Group.

Abstract-
      Text classification is one of the most important tasks in data mining. This paper investigates different variations of vector space models (VSMs) using KNN algorithm. The bases of our comparison are the most popular text evaluation measures. The Experimental results against the Saudi data sets reveal that Cosine outperformed Dice and Jaccard coefficients.

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Keywords-Arabic data sets, Data mining, Text categorization, Term weighting, VSM.