Machine Learning
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Comparison of three machine-learning methods for Thai part-of-speech tagging
ACM Transactions on Asian Language Information Processing (TALIP)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
PKIP: Feature Selection in Text Categorization for Item Banks
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
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We present PKIP, an adaptable learning assistant tool for managing question items in item banks. PKIP is not only able to automatically assist educational users to categorize the question items into predefined categories by their contents but also to correctly retrieve the items by specifying the category and/or the difficulty level. PKIP adapts the ''categorization learning model'' to improve the system's categorization performance using the incoming question items. PKIP tool has an advantage over the traditional document categorization methods in that it can correctly categorize the question item which lacks keywords since it adopts the feature selection technique and support vector machine approach to item bank text categorization. In our initial experimentation, PKIP was designed and implemented to manage the Thai high primary mathematics question items. PKIP was tested and evaluated in terms of both system accuracy and user satisfaction. The evaluation result shows that the system accuracy is acceptable and PKIP satisfies the need of the users.