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Power law distributions in information retrieval
Casper Petersen,
Jakob Grue Simonsen
,
Christina Lioma
Datalogisk Institut
Datalogisk Institut
22
Citationer (Scopus)
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Keyphrases
Information Retrieval
100%
Power-law Distribution
100%
Power Law
100%
Query Frequency
75%
Distribution Fitting
50%
Document Length
50%
Term Frequency
50%
Scale-free
25%
Citation Frequency
25%
Retrieval Task
25%
Information Retrieval Evaluation
25%
Closed Nature
25%
Generalized Extreme Value Distribution
25%
Probability Distribution
25%
Computational Cost
25%
Negative Binomial Distribution
25%
Frequency Distribution
25%
Empirical Probability
25%
Fat Tails
25%
Statistical Treatment
25%
Index Compression
25%
Potential Gains
25%
Engineering
Frequency Term
100%
Potential Gain
50%
Statistical Treatment
50%
Compression Index
50%
Gaussians
50%
Negative Value
50%
Computational Cost
50%
Mathematics
Power Law Distribution
100%
Power Law
100%
Document Length
28%
Probability Distribution
28%
Termfrequency
28%
Inverse Gaussian
14%
Extreme Value
14%
Frequency Distribution
14%
Negative Binomial
14%
Computational Cost
14%
Fat Tail
14%
Computer Science
Information Retrieval
100%
Approximation (Algorithm)
100%
Syntactics
33%
Computational Cost
33%
Research Question
33%
Negative Value
33%
Information Retrieval Evaluation
33%
Social Sciences
Information Retrieval
100%
Law
100%
Frequency Distribution
12%
Extreme Value
12%
Syntax
12%
Economics, Econometrics and Finance
Extreme Value
100%