Computation of logical effort in high level languages
Computer Languages
Requirements for query evaluation in weighted information retrieval
Information Processing and Management: an International Journal
Extended Boolean information retrieval
Communications of the ACM
The topological information retrieval system and the topological paradigm: a unification of the major models of information retrieval
A transient hypergraph-based model for data access
ACM Transactions on Information Systems (TOIS)
Natural language navigation in multimedia archives: an integrated approach
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Modeling vagueness in information retrieval
Lectures on information retrieval
Modeling Vagueness in Information Retrieval
ESSIR '00 Proceedings of the Third European Summer-School on Lectures on Information Retrieval-Revised Lectures
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Most document retrieval systems based on probabilistic models of feature distributions assume random selection of documents for retrieval. The assumptions of these models are met when documents are randomly selected from the database or when retrieving all available documents. A more suitable model for retrieval of a single document assumes that the best document available is to be retrieved first. Models of document retrieval systems assuming random selection and best-first selection are developed and compared under binary independence and two Poisson independence feature distribution models. Under the best-first model, feature discrimination varies with the number of documents in each relevance class in the database. A weight similar to the Inverse Document Frequency weight and consistent with the best-first model is suggested which does not depend on knowledge of the characteristics of relevant documents.