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Bias and the Limits of Pooling for Large Collections



C E. Buckley, Darrin L. Dimmick, Ian Soboroff, Ellen M. Voorhees


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. Yet a constant-size pool represents an increasingly small percentage of the document set as document sets grow larger, and at some point the assumption of approximately complete judgments must become invalid. This paper shows that the judgment sets produced by traditional pooling when the pools are too small relative to the total document set size can be biased in that they favor relevant documents that contain topic title words. This phenomenon is wholly dependent on the collection size and does not depend on the number of relevant documents for a given topic. We show that the AQUAINT test collection constructed in the recent TREC2005 workshop exhibits this biased relevance set; it is likely that the test collections based on the much larger GOV2 document set also exhibit the bias. The paper concludes with suggested modifications to traditional pooling and evaluation methodology that may allow very large reusable test collections to be built.
Information Retrieval


evaluation of information retrieval, information retrieval, test collections


Buckley, C. , Dimmick, D. , Soboroff, I. and Voorhees, E. (2007), Bias and the Limits of Pooling for Large Collections, Information Retrieval, [online], (Accessed July 14, 2024)


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Created July 16, 2007, Updated October 12, 2021