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Dynamic Sampling Meets Pooling

Published: 18 July 2019 Publication History

Abstract

A team of six assessors used Dynamic Sampling (Cormack and Grossman 2018) and one hour of assessment effort per topic to form, without pooling, a test collection for the TREC 2018 Common Core Track. Later, official relevance assessments were rendered by NIST for documents selected by depth-10 pooling augmented by move-to-front (MTF) pooling (Cormack et al. 1998), as well as the documents selected by our Dynamic Sampling effort. MAP estimates rendered from dynamically sampled assessments using the xinfAP statistical evaluator are comparable to those rendered from the complete set of official assessments using the standard trec_eval tool. MAP estimates rendered using only documents selected by pooling, on the other hand, differ substantially. The results suggest that the use of Dynamic Sampling without pooling can, for an order of magnitude less assessment effort, yield information-retrieval effectiveness estimates that exhibit lower bias, lower error, and comparable ability to rank system effectiveness.

References

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Cited By

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  • (2024)On the Evaluation of Machine-Generated ReportsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657846(1904-1915)Online publication date: 10-Jul-2024
  • (2023)HC3: A Suite of Test Collections for CLIR Evaluation over Informal TextProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591893(2880-2889)Online publication date: 19-Jul-2023
  • (2022)HC4: A New Suite of Test Collections for Ad Hoc CLIRAdvances in Information Retrieval10.1007/978-3-030-99736-6_24(351-366)Online publication date: 10-Apr-2022
  • Show More Cited By

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cover image ACM Conferences
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 18 July 2019

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  • Natural Sciences and Engineering Research Council of Canada

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2024)On the Evaluation of Machine-Generated ReportsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657846(1904-1915)Online publication date: 10-Jul-2024
  • (2023)HC3: A Suite of Test Collections for CLIR Evaluation over Informal TextProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591893(2880-2889)Online publication date: 19-Jul-2023
  • (2022)HC4: A New Suite of Test Collections for Ad Hoc CLIRAdvances in Information Retrieval10.1007/978-3-030-99736-6_24(351-366)Online publication date: 10-Apr-2022
  • (2019)Quantifying Bias and Variance of System RankingsProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331356(1089-1092)Online publication date: 18-Jul-2019

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