BM25 is a probabilistic information retrieval model that ranks documents based on term frequency, inverse document frequency, and document length normalization within a corpus.
119
Publications
9.8K
Citations
357
Authors
121
Institutions
Publications per year
2017–2026
52
Variants
Best Matching 25
Parents
Publications
119
Towards more accurate retrieval of duplicate bug reports
C. P. Sun, David Lo, Siau‐Cheng Khoo et al. · 2011 · 333 citations · Full text
Neural Ranking Models with Weak Supervision
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn et al. · 2017 · 309 citations · Full text
1–5 of 119
Authors
357
- NC
5
h-index
- SR
5
h-index
- CL
4
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- WB
4
h-index
- NT
4
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1–5 of 357
Institutions
121
Microsoft (United States)
Redmond, United States
7 h-index
18 publications
3K citations
University of Massachusetts Amherst
Amherst Center, United States
6 h-index
13 publications
1.4K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
6 h-index
13 publications
5.9K citations
University of Waterloo
Waterloo, Canada
6 h-index
16 publications
879 citations
University of Glasgow
Glasgow, United Kingdom
5 h-index
11 publications
435 citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
7 publications
612 citations
Chinese Academy of Sciences
Beijing, China
3 h-index
4 publications
258 citations
York University
Toronto, Canada
3 h-index
9 publications
219 citations
1–8 of 121
Venues
- arXiv (Cornell University)
Repository · 15 publications · 698 citations
12
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- Text REtrieval Conference
Conference · 3 publications · 70 citations
3
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- Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Conference · 2 publications · 80 citations
2
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- ACM Transactions on Information Systems
2 publications · 39 citations
2
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- Arrow@dit (Dublin Institute of Technology)
Repository · 2 publications · 23 citations
2
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1–5