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
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Probabilistic models of indexing and searching
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
Probabilistic approaches to the document retrieval problem
SIGIR '82 Proceedings of the 5th annual ACM conference on Research and development in information retrieval
Probabilistic search term weighting - some negative results
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference 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
Optimum probability estimation based on expectations
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR 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
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
Looking back: On relevance, probabilistic indexing and information retrieval
Information Processing and Management: an International Journal
Determining termhood for learning domain ontologies using domain prevalence and tendency
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Determining termhood for learning domain ontologies in a probabilistic framework
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
A probabilistic framework for automatic term recognition
Intelligent Data Analysis
A statistical view of binned retrieval models
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
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We describe two retrieval models for probabilistic indexing. The binary independence indexing (BII) model is a generalized version of the Maron & Kuhns indexing model. In this model, the indexing weight of a descriptor in a document is an estimate of the probability of relevance of this document with respect to queries using this descriptor. The retrieval-with-probabilistic-indexing (RPI) model is suited to different kinds of probabilistic indexing. Therefore we assume that each indexing model has its own concept of 'correctness' to which the probabilities relate. The concept of correctness is not necessarily identical with the concept of relevance, it is only required to depend on relevance. In addition to the probabilistic indexing weights, the RPI model provides the possibility of relevance weighting of search terms. Both retrieval models are compared in experiments, showing equally good results.