方法对比

	TREC Pooling and Relevance Judgments	Cranfield Evaluation Paradigm
领域	Library Information Science	Library Information Science
方法族	Process / pipeline	Process / pipeline
起源年份	2005	1967
提出者	Ellen M. Voorhees & Donna K. Harman (NIST TREC)	Cyril W. Cleverdon
类型	Pooled relevance-assessment pipeline for large test collections	Test-collection evaluation pipeline for retrieval effectiveness
开创性文献	Voorhees, E. M., & Harman, D. K. (Eds.). (2005). TREC: Experiment and Evaluation in Information Retrieval. MIT Press. ISBN: 9780262220736	Cleverdon, C. W. (1967). The Cranfield tests on index language devices. Aslib Proceedings, 19(6), 173-194.
别名	Pooling Method, Depth Pooling, TREC Pooling, Pooled Relevance Assessment	Cranfield Methodology, Test Collection Evaluation, Cranfield Tests, Laboratory IR Evaluation
相关	3	3
摘要	Pooling is the technique that lets the Cranfield evaluation paradigm scale to collections of millions of documents, where judging every document for every topic is impossible. Developed and institutionalized at the US National Institute of Standards and Technology for the Text REtrieval Conference (TREC), pooling gathers the top-ranked documents returned by many participating systems for each topic, merges them into a single pool, has human assessors judge only that pool, and treats every unjudged document as non-relevant. The result is a reusable test collection — documents, topics, and pooled relevance judgments (qrels) — on which new systems can later be scored without further assessment. Pooling is what made large-scale, reproducible retrieval evaluation feasible.	The Cranfield evaluation paradigm is the foundational experimental design for measuring how well an information retrieval system finds relevant documents. Devised by Cyril Cleverdon at the College of Aeronautics in Cranfield during the 1960s, it fixes three ingredients — a document collection, a set of search requests, and human relevance judgments linking requests to documents — and then holds them constant so that competing indexing methods or retrieval algorithms can be compared on recall and precision under controlled, repeatable conditions. By abstracting evaluation away from any single live user and turning it into a reusable laboratory experiment, Cranfield made retrieval effectiveness a measurable quantity and supplied the template that every later large-scale campaign, including TREC, has built upon.