摘要
查询扩展是针对信息检索中常见的"词不匹配"问题提出的一种优化方法。通过分析现有查询扩展方法的不足,提出一种基于半监督学习的查询扩展模型,该模型将查询扩展看作一个分类问题,并采用直推式支持向量机对样本进行训练。实验结果表明该方法进一步提高了搜索引擎的查全率和查准率。
Query expansion is a optimization method for ＂word mismatch＂ issues in information retrieval domain. By analyzing the shortcomings of existing methods, query expansion model based on semi-supervised learning is proposed, the model seems query expansion as a classification problem, and using transductvie support vector machine to train the samples. Experiments show that the recall and precision rates of search engine are further improved by this method.
出处
《计算机系统应用》
2012年第3期181-184,共4页
Computer Systems & Applications
关键词
信息检索
查询扩展
直推式支持向量机
半监督学习
SVM
information retrieve
query expansion
transductive support vector machines
semi-supervised learning
SVM
