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

Authors

43

  1. BH
    Ben He

    University of Chinese Academy of Sciences, China

    6 publications · 243 citations

    6

    h-index

  2. IO
    Iadh Ounis

    University of Glasgow, United Kingdom

    5 publications · 233 citations

    5

    h-index

  3. CM
    Craig Macdonald

    University of Glasgow, United Kingdom

    3 publications · 188 citations

    3

    h-index

  4. JP
    Jie Peng

    Shanghai Meteorological Bureau, China

    3 publications · 188 citations

    3

    h-index

  5. JG
    Jiafeng Guo

    Chinese Academy of Sciences, China

    2 publications · 50 citations

    2

    h-index

1–5 of 43

Venues

  1. 2

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  2. ACM Transactions on Information Systems

    2 publications · 900 citations

    2

    h-index

  3. Text REtrieval Conference

    Conference · 2 publications · 129 citations

    2

    h-index

  4. Advances in Complex Systems

    ACS · 1 publications · 88 citations

    1

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  5. Physical Review Letters

    PRL · 1 publications · 20 citations

    1

    h-index

1–5