Probabilistic Indexing is a document representation framework that assigns indexing weights to terms based on their estimated probability of relevance to future information needs, distinguishing it from deterministic or frequency-based indexing methods by incorporating probabilistic models of term relevance.
145
Publications
19.5K
Citations
245
Authors
121
Institutions
Publications per year
2017–2026
9
Parents
Publications
145
1–5 of 145
Authors
245
- NF
11
h-index
- SR
8
h-index
- KL
6
h-index
- ME
5
h-index
- EV
5
h-index
1–5 of 245
Institutions
121
University of California, Berkeley
Berkeley, United States
7 h-index
8 publications
1.8K citations
Cornell University
Ithaca, United States
5 h-index
6 publications
1.1K citations
IBM (United States)
Armonk, United States
5 h-index
8 publications
1.7K citations
Microsoft (United States)
Redmond, United States
4 h-index
9 publications
183 citations
Queen Mary University of London
London, United Kingdom
4 h-index
5 publications
274 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
4 h-index
6 publications
4K citations
TU Dortmund University
Dortmund, Germany
4 h-index
6 publications
1.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
7 publications
2.2K citations
1–8 of 121
Venues
- Journal of the American Society for Information Science
JASIS · 12 publications · 3.1K citations
10
h-index
- Information Processing & Management
9 publications · 1.5K citations
9
h-index
- ACM Transactions on Information Systems
5 publications · 760 citations
5
h-index
- Journal of Documentation
4 publications · 1.6K citations
4
h-index
- Information Retrieval
4 publications · 78 citations
4
h-index
1–5