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- W2898078244 abstract "Traditional practice recommends that information retrieval experiments be run over multiple test collections, to support, if not prove, that gains in performance are likely to generalize to other collections or tasks. However, because of the pooling assumptions, evaluation scores are not directly comparable across different test collections. We present a widely-used statistical tool, em meta-analysis, as a framework for reporting results from IR experiments using multiple test collections. We demonstrate the meta-analytical approach through two standard experiments on stemming and pseudo-relevance feedback, and compare the results to those obtained from score standardization. Meta-analysis incorporates several recent recommendations in the literature, including score standardization, reporting effect sizes rather than score differences, and avoiding a reliance on null-hypothesis statistical testing, in a unified approach. It therefore represents an important methodological improvement over using these techniques in isolation." @default.
- W2898078244 created "2018-10-26" @default.
- W2898078244 creator A5053747568 @default.
- W2898078244 date "2018-10-17" @default.
- W2898078244 modified "2023-10-14" @default.
- W2898078244 title "Meta-Analysis for Retrieval Experiments Involving Multiple Test Collections" @default.
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- W2898078244 doi "https://doi.org/10.1145/3269206.3271719" @default.
- W2898078244 hasPublicationYear "2018" @default.
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