NDLI: Arabic Script Web Documents Language Identification Using Decision Tree-ARTMAP Model

Content Provider	IEEE Xplore Digital Library
Author	Selamat, A. Ng Choon Ching Mikami, Y.
Copyright Year	2007
Description	Author affiliation: Univ. Teknologi Malaysia, Skudai (Selamat, A.; Ng Choon Ching)
Abstract	Automatic language identification (LID) is a topic of great significance in areas of intelligent and security, where the language identities of any related materials need to be identified before any information can be processed. When the recognition elements of any content is dynamic and obtained directly from written text, the language associated with each grammar item has to be identified using that text. Many methods have been proposed in the literature are focusing on Roman and Asian languages. This paper describes text-based language identification approaches on Arabic script. Two different approaches have been compared. The decision trees method commonly used in many application domain is firstly reviewed. We also applied a simple method for language identification that is based on adaptive resonance learning (ART) neural network. The experimented result shows that the decision tree model achieved highest accuracy than ARTMAP model. However, decision tree model may not reliable if the language used extends to others Arabic script compared to ARTMAP model. It is assumed that hybrid of both models will perform better and merit for further development.
Starting Page	721
Ending Page	726
File Size	186522
Page Count	6
File Format	PDF
ISBN	0769530389
DOI	10.1109/ICCIT.2007.402
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-21
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Materials science and technology Subspace constraints Natural languages Neural networks Management information systems Conference management Resonance Decision trees Information technology
Content Type	Text
Resource Type	Article

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