摘要
传统向量空间模型信息检索技术,只是统计关键词在文档中出现的频度,检索结果不能反映出文档的相关性.为了解决关键词检索时潜在语义的挖掘问题,提出了一种基于向量空间模型的潜在语义索引的改进算法.对比实验证明,该算法能够有效提高检索查准率.
The traditional vector space model of information retrieval technology,just statistical key words in the document frequency,search results do not reflect the relevance of the document.In order to solve semantic keyword search when the problem of mining potential,a vector space model based on Latent Semantic Indexing improved algorithm,comparative experiments show that the algorithm can effectively improve the retrieval precision.
出处
《陕西科技大学学报（自然科学版）》
2010年第5期151-154,158,共5页
Journal of Shaanxi University of Science & Technology
关键词
向量空间模型
潜在语义索引
信息检索
vector space model
latent semantic indexing
information retrieval
