期刊文献+

基于向量空间的信息检索系统的评价方法

Study on measure method of vector space information retrieval system
下载PDF
导出
摘要 针对基于向量空间的信息检索系统给出了两种计算方法:基于文献——关键词矩阵和文献查询——评价者矩阵的计算方法.查全率和查准率在衡量基于向量空间的信息检索系统时,存在不足之处,根据基于向量空间的信息检索系统输出的检索结果的特点,提出了一种新的评价方法:偏移距离法. In this paper, two new methods that can calculate the recall and precision of the vector space information retrieval system is presented. Uses document keyword matrix or document assessor matrix to calculate the recall and precision . As the recall and precision have intrinsic defect to assess the vector space information retrieval system, puts forward the offset distance method to assess the vector space information retrieval system.
作者 吕鸿略
出处 《哈尔滨商业大学学报(自然科学版)》 CAS 2009年第4期436-438,共3页 Journal of Harbin University of Commerce:Natural Sciences Edition
关键词 查全率 查准率 偏移距离 信息检索 recall precision off set distance information retrieval
  • 相关文献

参考文献9

  • 1APRIL K, WILLIAM M P. A framework for understanding Latent Semantic Indexing performance [ J ]. Information Processing & Management,2006 ,d2 ( 3 ) :57 - 73.
  • 2CHEN S J, CHEN S M. Fuzzy Information Retrieval Based on Geometirc - Mean Averaging Operators[ J ]. Computers & Mathematics with Application ,2005,49 : 1213 - 1231.
  • 3FAN W G. A generic ranking function discovery framework by genetic programming for information retrieval[ J]. Information Processing & Management ,2004,40:587 - 602.
  • 4SALTOA G. A Simple Blueprint for Automation Boolean Query Formulation [ J ]. Information Processing & Management, 1983, 24:34 -51.
  • 5韩毅.基于文档结构的向量空间检索模型研究[J].情报学报,2004,23(2):158-162. 被引量:11
  • 6RICARDO BY,BERTHIER RN著,王知津等译.现代信息检索[M].北京:机械工业出版社,2005.102-104.
  • 7鲁松,李晓黎,白硕,王实.文档中词语权重计算方法的改进[J].中文信息学报,2000,14(6):8-13. 被引量:120
  • 8康海燕,樊孝忠,李彦芳,耿增民.熵原理在信息检索中的应用[J].计算机工程,2005,31(9):155-156. 被引量:3
  • 9任美睿,郭龙江,李金宝.基于改进的向量空间模型的自动文本分类[J].哈尔滨商业大学学报(自然科学版),2006,22(1):77-80. 被引量:2

二级参考文献25

  • 1PLATT J C.Fast training of support vector machines using sequential minimal optimization[A].Advances in Kernel Methods-Support Vector Learning[C].Cambridge,MA:MIT Press,1999.185-208.
  • 2SALTON G.Developments in automatic text retrieval[J].Science,1991,253(8):974-979.
  • 3SALTON G,ALLEN J,BUCKLEY C,et al.Automatic analysis,theme generation,and summarization of machine-readable texts[J].Science,1994,264(3):1421-1426.
  • 4SALTON G,BUCKLEY C.Term weighting approaches in automatic text retrieval[J].Information Processing and Management,1988,24(5):513-523.
  • 5NIGAM K,MCCALLUM A,THRUN S,et al.Text classification from labeled and unlabeled documents using EM[J].Machine Learning,2000,39(2-3):103-134.
  • 6任美睿 李建中 杨艳.基于朴素贝叶斯方法的自动文本分类系统的实现[J].计算机科学,2002,(8):285-87.
  • 7THERRIEN,CHARLES W.Debision,estimation and classification[M].New York:John Wiley & Sons Inc,1989.
  • 8Yang Yiming,ProceedingsoftheSeventeenthInternationalACMSIGIRConferenceonResearchandDevelopme,1994年,12页
  • 9(美)KantardzicM.闪叫清 陈茵 程雁等译.数据挖掘-概念、模型、方法和算法[M].北京:清华大学出版社,2003.08.36-56.
  • 10Jain A K, Duin R P W, Mao J. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(1):4-37.

共引文献136

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部