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Methods for ranking information retrieval systems without relevance judgments
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Proceedings of the 2003 ACM symposium on Applied computing table of contents
Melbourne, Florida
SESSION: Information access and retrieval table of contents
Pages: 811 - 816  
Year of Publication: 2003
ISBN:1-58113-624-2
Authors
Shengli Wu  University of Strathclyde, Glasgow, UK
Fabio Crestani  University of Strathclyde, Glasgow, UK
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 8,   Downloads (12 Months): 42,   Citation Count: 8
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ABSTRACT

In this paper we present some new methods of ranking information retrieval systems without relevance judgement. The common ground of these methods is using a measure we called reference count. An extensive experimentation was conducted to evaluate the effectiveness of the proposed methods using various different standards Information Retrieval evaluation measures for the ranking, like average precision, R-precision, and precision and different document levels. We also compared the effectiveness of the proposed methods with the method proposed by Soboroff et al. The experimental results showed that the proposed methods are effective, and in many cases are more effective than Soboroff at al.'s method.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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J. A. Shaw and E. A. Fox. Combination of multiple searches. In Proceedings of 3rd Text Retrieval Conference (TREC-3), pages 267--275, Gaithersburg, Maryland, USA, April 1995.
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E. M. Voorhees, editor. Proceedings of 10th Text Retrieval Conference, Gaithersburg, Maryland, USA, November 2001. National Technical Information Service of USA.
 
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E. M. Voorhees and D. K. Harman, editors. Proceedings of 8th Text Retrieval Conference, Gaithersburg, Maryland, USA, November 1999. National Technical Information Service of USA.
 
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CITED BY  8
 
 
 
 
 

Collaborative Colleagues:
Shengli Wu: colleagues
Fabio Crestani: colleagues

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