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现代图书情报技术  2004, Vol. 20 Issue (8): 44-47     https://doi.org/10.11925/infotech.1003-3513.2004.08.12
  信息检索技术 本期目录 | 过刊浏览 | 高级检索 |
相关反馈技术中的检索词排序算法
宋玲丽  成颖
(南京大学信息管理系  南京 210093)
About Term Ranking Methods in Relevance Feedback
Song Lingli   Cheng Ying
(Department of Information Management, Nanjing University, Nanjing 210093, China)
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摘要 

相关反馈是近年来信息检索领域的研究热点,是自动查询扩展中的一种重要形式,相关反馈主要包括检索词加权和检索词选择。本文介绍了在相关反馈技术中经典的检索词排序算法,对它们带来的性能改进做了比较,并提出了相关反馈的实际应用中需要解决的一些问题。

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关键词 相关反馈检索词排序检索词加权信息检索    
Abstract

One common method for query expansion is relevance feedback, and much research work has been done to it. Relevance feedback focuses two aspects: term reweighting and term selection. In this paper, the authors elaborate a few classical term ranking methods, and compare them in retrieval performance. At last, they discuss the problems in implementing relevance feedback.

Key wordsRelevance feedback    Term ranking    Term weighting    Information retrieval
收稿日期: 2004-03-17      出版日期: 2004-08-25
: 

G354

 
通讯作者: 宋玲丽     E-mail: lingli_song@163.com
作者简介: 宋玲丽,成颖
引用本文:   
宋玲丽,成颖. 相关反馈技术中的检索词排序算法[J]. 现代图书情报技术, 2004, 20(8): 44-47.
Song Lingli,Cheng Ying. About Term Ranking Methods in Relevance Feedback. New Technology of Library and Information Service, 2004, 20(8): 44-47.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2004.08.12      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2004/V20/I8/44

1  Rocchio J.J. Relevance Feedback in Information Retrieval. In Salton G.(Ed.),The SMART Retrieval System. 1971, Englewood Cliffs, N.J.: Prentice-Hall,Inc. pp313-323
2  Ed Greengrass. Information Retrieval: A Survey (2000).
http://citeseer.nj.nec.com/greengrass00information.html(Accessed Feb.17,2004)
3  Buckley B.C., Singhal A., Mitra M. et.al. New Retrieval Approaches us
ing SMART:TREC-4. In Harman D. ,editor,TREC-4, Proceedings of the Second Text REtrieval Conference .NIST Special Publication 1995:500-536
4  Harper D.J., van Rijsbergen C.J. An Evaluation of Feedback in Document Retrieval Using Co-Occurrence Data. Journal of Documentation,1978,34(3):189-216
5  Smeaton A.F., van Rijsbergen C.J. The Retrieval Effects of Query Expansion on aFeedback Document Retrieval System. The Computer Journal,1983,26(3):239-246
6  Harper D.J. Relevance Feedback in Document Retrieval System: An evaluation of Probabilistic Strategies. Doctoral Dissertation, Jesus College ,Cambrige,England,1980
7  Harman D. Towards Interactive Query Expansion. Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1988:321-331
8  Harman,D. Relevance feedback revisited. Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1992:1-10
9  Buckley,C., Salton,G., Allan,J. The effect of adding relevance information in a relevance feedback environment. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1994:292-300
10  Haines D., Croft W. B. Relevance feedback and inference networks. Proceedings of the 16th
annual international ACM SIGIR conference on Research and development in information retrieval,1993:2-11
11  Carpineto C., Romano G., et.al. Improving Retrieval Feedback with Multiple Term-Ranking. ACM Transactions on Information Systems,2002,20(3):259-290
12  Sparck Jones K. Search Term Relevance Weighting Given Little Relevance Information. Journal of Documentation,1979,35(1):30-48
13  Porter M.F., Galpin V. Relevance Feedback in a Public Access Catalogue for a Library:Muscat at the Scott Polar Research Institute. Program,1988,22(1):1-20
14  van Rijsbergen C.J., Harper D.J., Porter M.F. The Selection of Good Search Terms. Information Processing and Management,1981,17(2):77-91
15  Robertson S.E. On Term Selection for Query Expansion. Journal of Documentation,1990,46(4):359-364
16  Selberg E.W. Information Retrieval Advances using Relevance Feedback.
http://www.selberg.org/homes/speed/papers/generals/generals.pdf
17  Adesina A. M., Jones J. F. Applying summarization techniques for term selection in relevance feedback. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval,2001:1-9
18  Incorporating User Search Behavior into Relevance Feedback. Journal of the
American Society for Information Science and Technology.2003,54(6):529-549
19  Vakkari, P. Cognition and Changes of Search Terms and Tactics during Task Performance.
Proceedings of RIAO Conference on Content-Based Multimedia Information Access , 2001:894-907

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