Please use this identifier to cite or link to this item: https://ir.sc.mahidol.ac.th/handle/123456789/609
Title: Recovering "lack of words" in text categorization for item banks
Authors: Kanlaya Naruedomkul
Keywords: Text categorization (TC);Textual data
Issue Date: 2005
Publisher: 29th Annual International Computer Software and Applications Conference, COMPSAC 2005
Citation: คณิตศาสตร์
Series/Report no.: ;31-32
Abstract: PKIP, Patterned Keywords in Phrase, is our feature selection approach to text categorization (TC) for item banks. An item bank is a collection of textual data in which each item consists of short sentences and has only a few relevant words for categorization. Traditional TC techniques cannot provide sufficiently accurate resulte because of a "lack of words" problem. PKIP improves categorization accuracy and recovers from the "lack of words" problem. Our sample item bank is the collection of Thai primary mathematics problems and we use SVM as our classifier. Classification results show that PKIP produces acceptable classification performance.
Description: Scopus
URI: https://ir.sc.mahidol.ac.th/handle/123456789/609
ISSN: 07303157
Appears in Collections:Mathematics: International Proceedings

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