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Weirdness Coefficient as a Feature Selection Method for Arabic Special Domain Text Classification

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4 Author(s)
Abdulmohsen Al-Thubaity ; Comput. Res. Inst., King Abdulaziz City for Sci. & Technol., Riyadh, Saudi Arabia ; Albandari Alanazi ; Itisam Hazzaa ; Haya Al-Tuwaijri

Given the importance of organizing and managing the rapid growth in knowledge of Arabic electronic content, this study introduces the Weirdness Coefficient (W) as a new feature selection method for Arabic special domain text classification. The proposed method was used to classify a dataset comprising five Islamic topics using Naive base (NB) and K-nearest neighbor (K-NN) classifiers, and three representation schemas. The results were also compared with a well-known feature selection method, Chi-squared. In addition to its simplicity in computation, the Weirdness Coefficient showed promising classification accuracy.

Published in:

Asian Language Processing (IALP), 2012 International Conference on

Date of Conference:

13-15 Nov. 2012