Universiti Teknologi Malaysia Institutional Repository

Improved letter weighting feature selection on arabic script language identification

Ng, Choon-Ching and Selamat, Ali (2009) Improved letter weighting feature selection on arabic script language identification. In: 1st Asian Conference on Intelligent Information and Database System (ACIIDS 09), 2009, Quan Binh University, Dong Hoi City, Vietnam .

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Official URL: http://dx.doi.org/10.1109/ACIIDS.2009.33

Abstract

Language identification is the process identifying predefined language in a document automatically; we focused on the Web documents in this paper. Initially, we have applied the letter frequency as features combine with neural networks in Arabic script language identification. However, reliability of selected letters of the features is a major issue to be overcome. Therefore, we propose an improved letter weighting feature selection in order to enhance the effectiveness of language identification. It is based on the concept letter frequency document frequency. From the experiments, we have found that the improved letter weighting feature selection achieve the highest accuracy 99.75% on Arabic script language identification.

Item Type:Conference or Workshop Item (Paper)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:15289
Deposited By: Liza Porijo
Deposited On:22 Sep 2011 09:49
Last Modified:22 Sep 2011 09:49

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