Automatic Question Generation with Natural Language Processing and Genetic Algorithm

M. E. Sanap, S. N. Shelke, Saurabh K. Kurhade, Jeevan J. Mahajan, Rahul Kumar

Abstract


This thesis report describes an intelligent multiple- choice question examination system, named Automatic MCQ Generation, for students . It requires lots of educators efforts  and time since it is tedious and meticulous which can sometimes leads to human mistake .

The purpose of this research is to ease the educators work      in the process of preparation exam questions and giving them more time to concentrate on teaching materials and strengthen their teaching techniques without being burdened with the exam questions preparations.The area of research is multiple choice questions, which are commonly used in educational tests, social surveys, market research and other areas We introduce a rule- based automatic question generation for the task, as well as im- plement statistical sentence selection and various configurations of named entity recognition . Three types of WH-questions (What, Who, and Where) can be produced by our system .


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References


Simkim, M. (2005), ”Multiple Choice Test And Student Understand- ing:What is the connection?” - Decision Science Journal of Innovative Education.

Suraj Kamya, Madhuri Sachdeva, Navdeep Dhaliwal and Sonit Singh, Fuzzy Logic Based Intelligent Question Paper Generator IEEE Interna- tional Advance Computing Conference (IACC), 2014.

Demetriulias, M. M. (1982). Constructing Test Questions for Higher Level Thinking. Lippicott-Raven.

D. Liu, J. Wang and L. Zheng, ”Automatic Test Paper Generation Based on Ant Colony Algorithm,” Journal of Software, vol. 8, no. 10, 2013.

N. H. I. Teo, N. A. Bakar and M. R. A. Rashid, ”Representing Exam- ination Question Knowledge into Genetic Algorithm,” in IEEE Global Engineering Education Conference (EDUCON), Istanbul, 2014.

D. Hermawanto, ”Cornell University Library,” 8 2013. [Online]. Avail- able: http://arxiv.org/ftp/arxiv/papers/1308/1308.4675.pdf.

L. Jacobson, ”Creating,” 12 2 2012. [Online]. Available: http://www.theprojectspot.com/tutorial-post/creating-a-geneticalgorithm- for-beginners/3. [Accessed 18 12 2015].

Heilman, M. and Smith, N. A. (2010a). Extracting simplified statements for factual question generation. In Proc. of the Third Workshop on Question Generation.

Michael Heilman, CMU-LTI-11-004, ”Automatic Factual Question Gen- eration from Text”, 2011.


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