Rihong Wang
ABSTRACT
Text classification primarily from learning these two classifications based on semantic understanding and based on supervised machine to consider. Questions also consist of text, so the questions to achieve automatic classification are the classification text, classification questions help to improve the accuracy of automatic test paper to facilitate question bank management. This paper presented a hybrid model which mixing improved Semantic Comprehension and Machine Learning, and introduced as a word frequency correction index, the dispersion degree and positive and negative correlation coefficient to improve mutual information selection algorithm. Finally, it designed a construction testing training system questions classification module based on the framework, and applied to question classification test. The experiments show that the hybrid framework model improves the efficiency of automatic classification of questions with better classification accuracy.
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Index Terms
- Research of Test Questions Classification Based on Hybrid Frame Mixing Semantic Comprehension and Machine Learning
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