An Intelligent Test Paper Generation Method to Solve Semantic Similarity Problem

[摘要] In order to solve the problem of semantic duplication in the Intelligent Test Paper Generation Method, Genetic Particle Swarm Optimization Algorithm was used to search multi groups of test papers that conform to the constraints of the test. Then the idea of density entropy was used to screen the test papers, so that test papers will cover more questions uniformly. After that, analyze the semantic similarity of the test papers that use the TextRank algorithm to extract the key words of test questions, and use knowledge-keywords weighted VSM model to calculate the semantic similarity of test questions. Eliminate the high repetition rate of the test papers and avoid the repetition of the inspection points. The experimental results show that the algorithm can solve the semantic similarity problem in intelligent test paper generation of mass item bank.

[发布机构] Inst. of Netwk. Technol.; Eng. Res. Ctr. of Info. Netwk., Beijing Univ. of P. and Telecom.; Min. of Educ., Beijing, China^1;Institute of Network Technology Beijing University of Posts and Telecommunications Beijing, China^2

[效力级别] 材料科学

[关键词] Genetic particle swarm optimizations;High repetition rate;Intelligent test;Item bank;Key words;Multi-group;Semantic similarity;Test paper