Vertaile menetelmiä

	Cranfield Evaluation Paradigm	Query Expansion Evaluation
Tieteenala	Library Information Science	Library Information Science
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1967	2008
Kehittäjä	Cyril W. Cleverdon	Information retrieval evaluation tradition (Salton; Manning, Raghavan & Schütze)
Tyyppi	Test-collection evaluation pipeline for retrieval effectiveness	Evaluation pipeline for query-expansion effectiveness
Alkuperäislähde	Cleverdon, C. W. (1967). The Cranfield tests on index language devices. Aslib Proceedings, 19(6), 173-194.	Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. ISBN: 9780521865715
Rinnakkaisnimet	Cranfield Methodology, Test Collection Evaluation, Cranfield Tests, Laboratory IR Evaluation	Query Reformulation Evaluation, Term Expansion Assessment, Expansion Effectiveness Measurement, Automatic Query Expansion Evaluation
Liittyvät	3	3
Tiivistelmä	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.	Query expansion evaluation measures whether adding terms to a user's query — drawn from a thesaurus, from corpus co-occurrence statistics, or from relevance or pseudo-relevance feedback — actually improves retrieval. Expansion attacks the vocabulary-mismatch problem, where relevant documents use words the searcher did not, and it tends to raise recall by bringing in synonymous and related terms. But it can also lower precision by introducing ambiguity, and it can help some queries while badly hurting others. Sound evaluation therefore reports not just the average effectiveness change but the recall-precision trade-off and a robustness analysis of how many individual queries were helped versus harmed.