基于多示例学习的题库重复性检测研究

Itembank Redundancy Checking Based on Multi-Instance Learning

基于多示例学习方法对题库重复性检测算法进行了改进,其基本思想是:将包含多个子问题的试题重复性检测转化为多示例学习问题.采用基于前缀树的高频词抽取算法抽取试题的内容特征,避免了对同义词典的依赖.在此基础上,结合试题的元数据特征提出试题相似度计算方法.在真实题库基础上进行的实验结果显示,该方法简便可行,正确率和查全率分别达到91.3%和92.3%,为进一步实现题库系统的整合奠定了基础.

A method based on multi-instance learning to improve the itembank redundancy checking algorithm is proposed. Redundancy checking for items with multiple questions is addressed through transforming it into a multi-instance learning problem. High-frequency words addressed through transforming it into a multi-instance learning problem. High-frequency words extracting algorithm based on suffix tree is used to extract content features of items and the use of thesaurus can be avoided. Combined with metadata features of items item similarity is proposed. Experiments on the realworld itembank , a method to compute dataset show that the proposed method is an effective and feasible solution to the itembank redundancy checking problem, and achieves 91.3% precision and 92.3% recall. It laid groundwork for future work on the integration of itembank systems.