Retrieval test evaluation of a rule based automatic indexing (AIR/PHYS)
Proc. of the third joint BCS and ACM symposium on Research and development in information retrieval
Random and best-first document selection models
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
The automatic indexing system AIR/PHYS - from research to applications
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
Two models of retrieval with probabilistic indexing
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
The maximum entropy principle in information retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
A clustered search algorithm incorporating arbitrary term dependencies
ACM Transactions on Database Systems (TODS)
Precision Weighting—An Effective Automatic Indexing Method
Journal of the ACM (JACM)
Evaluation of the 2-Poisson model as a basis for using term frequency data in searching
SIGIR '83 Proceedings of the 6th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models of indexing and searching
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
An evaluation of term dependence models in information retrieval
SIGIR '82 Proceedings of the 5th annual ACM conference on Research and development in information retrieval
Optimum polynomial retrieval functions based on the probability ranking principle
ACM Transactions on Information Systems (TOIS)
Optimum polynomial retrieval functions
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
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Probability estimation is important for the application of probabilistic models as well as for any evaluation in IR. We discuss the interdependencies between parameter estimation and other properties of probabilistic models. Then we define an optimum estimate which can be applied to various typical estimation problems in IR. A method for the computation of this estimate is described which uses expectations from empirical distributions. Some experiments show the applicability of our method, whereas comparable approaches are partially based on false assumptions or yield estimates with systematic errors.