Chi Square Feature Extraction Based Svms Arabic Language Text Categorization System

  title={Chi Square Feature Extraction Based Svms Arabic Language Text Categorization System},
  author={Abdelwadood Moh'd and A. M. Mesleh},
This paper aims to implement a Support Vector Machines (SVMs) based text classification system for Arabic language articles. This classifier uses CHI square method as a feature selection method in the pre-processing step of the Text Classification system design procedure. Comparing to other classification methods, our system shows a high classification effectiveness for Arabic data set in term of F-measure (F=88.11). 
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Publications referenced by this paper.
Showing 1-10 of 22 references

An evaluation of statistical approaches to text categorization

  • Y. Yang, Ming
  • Inform Retrieval
  • 1999
Highly Influential
5 Excerpts

A New Technique for Automatic Text Categorization for Arabic Documents, 5 IBIMA Conference (The internet & information technology in modern organizations)

  • A. Samir, W. Ata, N. Darwish
  • 2005
3 Excerpts

Introduction to Machine Learning, Draft Version 1.1.5

  • T. Hofmann
  • November 10,
  • 2003
2 Excerpts

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