Abstract

Question classification is the process by which a system analyzes a question and labels the question based on the category to which it belongs. The automated categorization (or classification) of questions into predefined categories has witnessed a booming interest due to the increased popularity of web technologies. The recent advancement in the form of E-Learning calls for the need of question categorization. In network based learning the questions posted by students need to be categorized on the basis of the concerned concepts. This point to the relevance of question categorization in this area. Many approaches to question classification have been proposed and have achieved reasonable results. The dominant approaches are machine learning and context based classification. There are several Machine Learning methods for question categorization. Here we are extending the previous methods for text categorization to question categorization and making a comparative study of the performance of two approaches, Naïve Bayes and Support Vector Machine

References

• HAYES, P. J. , ANDERSEN, P. M. , NIRENBURG, I. B. , AND SCHMANDT, L. M. Tcs: shell for content-based text categorization. In Proceedings of CAIA-90, 6th IEEE Conference on Artificial Intelligence Applications (Santa Barbara, CA, 1990), 320–326. , 1990
• ETHEM,ALPAYDIN,Introduction to Machine Learning, MIT Press, Cambridge. ,2010.
• XIAOSHAN,PAN. , AND FRANZ ,J. KURFESS. A Context-Based Free Text Interpreter,California Polytechnic State University San Luis Obispo Master's Thesis - Computer Science Department
• S. DUMAIS. ,Improving the retrieval information from external sources. Behavior Research Methods, Instruments and Computers, 23:229–236. , 1991.
• KOLLER, D. , AND SAHAMI, M. Hierarchically classifying documents using very few words. In Proceedings of ICML-97, 14th International Conference on Machine Learning (Nashville, TN, 1997), 170–178. ,1997.
• J. KIVINEN, M. WARMUTH. , and P. AUER. The perceptron algorithm vs. winnow: Linear vs. logarithmic mistake bounds when few input variables are relevant. In Conference on Computational Learning Theory. , 1995.
• T. MITCHELL. Machine Learning. McGraw-Hill, New York,NY. , 1997.
• SEBASTIANI,F. Machine Learning in Automated Text Categorization, Consiglio Nazionale delle Ricerche, Italy. ,ACM Computing Surveys. ,2002.
• HUANG,P. ,BU,J. J. , CHEN,C. , AND QIU, GUANG. An Effective Feature-Weighting Model for Question Classification,International Conference on Computational Intelligence and Security. ,2007