Comparer des méthodes

	TREC Pooling and Relevance Judgments	Query Expansion Evaluation
Domaine	Library Information Science	Library Information Science
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005	2008
Auteur d'origine	Ellen M. Voorhees & Donna K. Harman (NIST TREC)	Information retrieval evaluation tradition (Salton; Manning, Raghavan & Schütze)
Type	Pooled relevance-assessment pipeline for large test collections	Evaluation pipeline for query-expansion effectiveness
Source fondatrice	Voorhees, E. M., & Harman, D. K. (Eds.). (2005). TREC: Experiment and Evaluation in Information Retrieval. MIT Press. ISBN: 9780262220736	Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. ISBN: 9780521865715
Alias	Pooling Method, Depth Pooling, TREC Pooling, Pooled Relevance Assessment	Query Reformulation Evaluation, Term Expansion Assessment, Expansion Effectiveness Measurement, Automatic Query Expansion Evaluation
Apparentées	3	3
Résumé	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.	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.