在线考试系统中试题上传方法的优化

试题库是在线考试系统的数据基础。现有的试题上传方法需人工预处理操作，代价高，效率低。针对这一问题，提出了一种基于语义的高效、智能解析方法IDP（Intelligent Document Parsing）。IDP根据试题的类型特征，定义解析规则，并对所有段落进行规则匹配，实现类型识别。此外，针对特定类型的试题，进行分割段落、语段分析，从而完成智能解析，实现试题上传功能。实验结果表明，IDP省去了人工预处理的步骤，可以直接使用任意格式的试题资源文档完成上传任务，效率明显提升，具有很好的通用性和实用性。

Optimization of question upload method in online examination system

Question database is the base of online examination system. Existing methods of question upload need artificial pretreatment which makes them costly and inefficient. In order to solve this problem, a semantic-based efficient and intelligent method named Intelligent Document Parsing（IDP） is proposed. According to the characteristics of questions, IDP defines analytical rules, matching all paragraphs using the analytical rules, and identifying the types. Moreover, for the questions of specific types, it splits the paragraph and analyzes every phrase so as to complete the question upload function. Experimental results show that IDP eliminates the step of manual typesetting in the conventional method, uses question documents of all forms directly to complete the upload task, and improves the efficiency significantly. It has high versatility and practicality.