基于向量空间模型的知识点与试题自动关联方法  

Automatic Discovery of Relationship Between Examination Question and Knowledge Points Based on Vector Space Model

作　　者：董奥根 刘茂福[1] 黄革新[1] 舒琦赟 

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430081

出　　处：《计算机与现代化》2015年第10期6-9,共4页Computer and Modernization

基　　金：国家自然科学基金资助项目(61100133);国家社会科学基金重大项目(11&Z189);武汉科技大学大学生科技创新基金资助项目(14ZRA076)

摘　　要：随着基于知识点题库系统的广泛应用,实现试题与知识点的自动匹配,成为当下研究的重要问题。利用word2vec分别得到试题和知识点所包含的每个词的K维空间向量,通过计算空间向量间的余弦距离得到题干与知识点的语义相似度,进而自动发现知识点与试题之间的语义关联关系。实验结果显示此方法能快速发现试题与知识点的关系,提高题库系统的工作效率。With the extensive application of examination system based on knowledge points,how to automatically match examination question with knowledge points has become an important direction of current research. In this paper,word2 vec is used to get the K-dimensional space vector for each word in the texts of examination question and the knowledge points at first. And then,we calculate the cosine distance between the vectors to represent the semantic similarity of the examination question and the knowledge points. The experimental results show that this method can quickly find the relationship between examination question and knowledge points and improve the work efficiency of the examination system.

关 键 词：word2vec 知识点 余弦距离 语义相似度 向量空间模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]