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Variations in relevance judgments and the measurement of retrieval effectiveness
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Citations
Cited by
Test Collection Based Evaluation of Information Retrieval Systems
Sanderson, M. (2010) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000009 · Cited by 485
The Philosophy of Information Retrieval Evaluation
Voorhees, E. M. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45691-0_34 · Cited by 389
Self-citationApproximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K.-F., Liu, J., Bennett, P., Ahmed, J., & Overwijk, A. (2020) · arXiv · doi:10.48550/arxiv.2007.00808 · Cited by 204
Evaluation of machine-learning protocols for technology-assisted review in electronic discovery
Cormack, G. V., & Grossman, M. R. (2014) · Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval · doi:10.1145/2600428.2609601 · Cited by 149
Engineering Quality and Reliability in Technology-Assisted Review
Cormack, G. V., & Grossman, M. R. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911510 · Cited by 98
FAST2: An intelligent assistant for finding relevant papers
Yu, Z., & Menzies, T. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.021 · Cited by 80
User Simulation for Evaluating Information Access Systems
Balog, K., & Zhai, C. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000098 · Cited by 52
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?
Rodriguez, P., Barrow, J., Hoyle, A. M., Lalor, J. P., Jia, R., & Boyd-Graber, J. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.346 · Cited by 47
Multi-Faceted Recall of Continuous Active Learning for Technology-Assisted Review
Cormack, G. V., & Grossman, M. R. (2015) · Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/2766462.2767771 · Cited by 38
Metadata
- Authors
- Voorhees, E. M.
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2000-09
- Volume
- 36
- Issue
- 5
- Pages
- 697-716
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W2120308175
- Cited by
- 504
- References
- 0
- FWCI
- 51.05
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.