Divergence From Randomness
Divergence From Randomness is a probabilistic retrieval framework that models term significance in documents by measuring the extent to which term frequency distributions diverge from randomness, thereby distinguishing informative terms from those likely to occur by chance.
19
Publications
1.8K
Citations
43
Authors
16
Institutions
Publications per year
2017–2026
2
Parents
19
Lower-bounding term frequency normalization
Yuanhua Lv, ChengXiang Zhai · 2011 · 151 citations
Thomas Roelleke, Jun Wang · 2008 · 112 citations
Ramon Ferrer‐i‐Cancho, Ricard V. Solé · Advances in Complex Systems · 2002 · 88 citations · Full text
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University of Glasgow
Glasgow, United Kingdom
6 h-index
19 publications
1.9K citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
9 publications
234 citations
Universitat Politècnica de Catalunya
Barcelona, Spain
2 h-index
3 publications
262 citations
Institute of Computing Technology
Beijing, China
1 h-index
6 publications
204 citations

University of Edinburgh
Edinburgh, United Kingdom
1 h-index
2 publications
40 citations
Yahoo (Spain)
Madrid, Spain
1 h-index
1 publications
59 citations

University of Stirling
Stirling, United Kingdom
1 h-index
1 publications
20 citations
Hangzhou Dianzi University
Hangzhou, China
1 h-index
1 publications
10 citations
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JASIST · 2 publications · 54 citations
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2 publications · 900 citations
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Conference · 2 publications · 129 citations
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ACS · 1 publications · 88 citations
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PRL · 1 publications · 20 citations
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