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Bias and the limits of pooling
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Seattle, Washington, USA
POSTER SESSION: Posters table of contents
Pages: 619 - 620  
Year of Publication: 2006
ISBN:1-59593-369-7
Authors
Chris Buckley  Sabir Research, Inc
Darrin Dimmick  National Institute of Standards and Technology
Ian Soboroff  National Institute of Standards and Technology
Ellen Voorhees  National Institute of Standards and Technology
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 5,   Downloads (12 Months): 69,   Citation Count: 9
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ABSTRACT

Modern retrieval test collections are built through a process called pooling in which only a sample of the entire document set is judged for each topic. The idea behind pooling is to find enough relevant documents such that when unjudged documents are assumed to be nonrelevant the resulting judgment set is sufficiently complete and unbiased. As document sets grow larger, a constant-size pool represents an increasingly small percentage of the document set, and at some point the assumption of approximately complete judgments must become invalid.This paper demonstrates that the AQUAINT 2005 test collection exhibits bias caused by pools that were too shallow for the document set size despite having many diverse runs contribute to the pools. The existing judgment set favors relevant documents that contain topic title words even though relevant documents containing few topic title words are known to exist in the document set. The paper concludes with suggested modifications to traditional pooling and evaluation methodology that may allow very large reusable test collections to be built.



CITED BY  9

Collaborative Colleagues:
Chris Buckley: colleagues
Darrin Dimmick: colleagues
Ian Soboroff: colleagues
Ellen Voorhees: colleagues