Dense Vector Retrieval is a vector space retrieval method that represents documents and queries as dense, low-dimensional vectors derived from neural network embeddings, distinguishing itself from sparse vector models by capturing semantic relationships through continuous vector representations.
21
Publications
4K
Citations
89
Authors
30
Institutions
Publications per year
2017–2026
20
Parents
Publications
21
Ad Hoc Table Retrieval using Semantic Similarity
Shuo Zhang, Krisztian Balog · 2018 · 127 citations · Full text
Estimating Embedding Vectors for Queries
Hamed Zamani, W. Bruce Croft · 2016 · 97 citations
1–5 of 21
Authors
89
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1–5 of 89
Institutions
30
Renmin University of China
Beijing, China
3 h-index
10 publications
272 citations
Meta (Israel)
Tel Aviv, Israel
3 h-index
17 publications
3.3K citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
5 publications
130 citations
University College London
London, United Kingdom
1 h-index
1 publications
29 citations
Georgetown University
Washington, United States
1 h-index
3 publications
78 citations
University of Waterloo
Waterloo, Canada
1 h-index
3 publications
57 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
1 h-index
7 publications
322 citations
Institute of Software
Beijing, China
1 h-index
2 publications
52 citations
1–8 of 30
Venues
- arXiv (Cornell University)
Repository · 4 publications · 3.1K citations
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- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Conference · 2 publications · 62 citations
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- Transactions of the Association for Computational Linguistics
TACL · 1 publications · 19 citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 1 publications · 26 citations
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- Machine Intelligence Research
1 publications · 21 citations
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