Recovering "lack of words" in text categorization for item banks

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. © 2005 IEEE.