A probabilistic information retrieval method using the Maximum Entropy Principle (MEP) was proposed by Cooper and Huizinga (1982). Several refinements of the MEP for information retrieval have been proposed by Kantor and Lee (1986, 1991), but the MEP has not been evaluated in any large database. This article examines the MEP retrieval method using the TREC5 database. The MEP is evaluated by several tests and compared with a “naive ordering method” and “lexicographic ordering method.” The MEP does not provide any startling improvement, and it works reasonably well only in the case of a small number of keys and a relatively small collection. © 1998 John Wiley & Sons, Inc.

The maximum entropy principle may be applied to the design of probabilistic retrieval systems. When there are inconsistent expert judgments, the resulting optimization problem cannot be solved. The inconsistency of the expert judgments can be revealed by solving a linear programming formulation. In the case of inconsistent judgment, four plausible schemes are proposed in order to find revised judgments which are consistent with the true data structure but still reflect the original expert judgment. These schemes are the Interactive, Minimum Distance, Minimum Cross-Entropy, and Path methods. © 1991 John Wiley & Sons, Inc.

Applications, assumptions and properties of the maximum entropy principle are discussed. The maximum entropy principle integrates prior estimates of relevance with the observed distribution of term combinations. The result may be a reordering of the segments of a database, compared to a naive estimate. Numerical examples obtained by solution of the non-linear equations for the dual variables are presented and discussed.