Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization

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contributor.author Duwairi, Rehab en_US
contributor.author Al-Refai, Mohammad en_US
contributor.author Khasawneh, Natheer en_US
date.accessioned 2009-12-28T05:33:38Z en_US
date.available 2009-12-28T05:33:38Z en_US
date.issued 2007-11-18 en_US
identifier.citation Duwairi, R.; Al-Refai, M.; Khasawneh, N., "Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization," Innovations in Information Technology, 2007. IIT '07. 4th International Conference on , vol., no., pp.446-450, 18-20 Nov. 2007 en_US
identifier.uri http://dx.doi.org/10.1109/IIT.2007.4430403 en_US
identifier.uri http://hdl.handle.net/10576/10501 en_US
description.abstract This paper compares and contrasts two feature selection techniques when applied to Arabic corpus; in particular; stemming, and light stemming were employed. With stemming, words are reduced to their stems. With light stemming, words are reduced to their light stems. Stemming is aggressive in the sense that it reduces words to their 3-letters roots. This affects the semantics as several words with different meanings might have the same root. Light stemming, by comparison, removes frequently used prefixes and suffixes in Arabic words. Light stemming doesn't produce the root and therefore doesn't affect the semantics of words; it maps several words, which have the same meaning to a common syntactical form. The effectiveness of above two feature selection techniques was assessed in a text categorization exercise for Arabic corpus. This corpus consists of 15000 documents that fall into three categories. The K-nearest neighbors (KNN) classifier was used in this work. Several experiments were carried out using two different representations of the same corpus; the first version uses stem- vectors; and the second uses light stem-vectors as representatives of documents. These two representations were assessed in terms of size, time and accuracy. The light stem representation was superior in terms of classifier accuracy when compared with stemming. en_US
language.iso en en_US
publisher IEEE en
subject Arabic language en_US
subject K-nearest neighbors classifier en_US
subject feature selection en_US
subject light-stemming en_US
subject stemming en_US
subject text categorization en_US
title Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization en_US
type Article en_US


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