NDLI: The maximum entropy approach and probabilistic IR models

Content Provider	ACM Digital Library
Author	Greiff, Warren R. Ponte, Jay M.
Copyright Year	2000
Abstract	This paper takes a fresh look at modeling approaches to information retrieval that have been the basis of much of the probabilistically motivated IR research over the last 20 years. We shall adopt a subjectivist Bayesian view of probabilities and argue that classical work on probabilistic retrieval is best understood from this perspective. The main focus of the paper will be the ranking formulas corresponding to the Binary Independence Model (BIM), presented originally by Roberston and Sparck Jones [1977] and the Combination Match Model (CMM), developed shortly thereafter by Croft and Harper [1979]. We will show how these same ranking formulas can result from a probabilistic methodology commonly known as Maximum Entropy (MAXENT).
Starting Page	246
Ending Page	287
Page Count	42
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/352595.352597
Volume Number	18
Issue Number	3
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-07-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	$\textit{idf}$ weighting Binary independence model Combination match Linked dependence Probability ranking principle
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

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The maximum entropy approach and probabilistic IR models