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O'Connor, John – Amer Doc, 1969
Descriptors: Information Retrieval, Relevance (Information Retrieval)

Cook, Kenneth H. – Information Processing and Management, 1975
Describes a relevance decision model based on a threshold concept and techniques to validate the model which were developed through pretesting. (Author/PF)
Descriptors: Information Retrieval, Models, Relevance (Information Retrieval)

Robertson, S. E. – Journal of Documentation, 1977
The principle that, for optimal retrieval, documents should be ranked in order of the probability of relevance is discussed. (Author/KP)
Descriptors: Information Retrieval, Probability, Relevance (Information Retrieval)

Marcus, Richard S. – Journal of the American Society for Information Science, 1972
The purpose of this communication is to suggest that in the situation where documents are retrieved independently the desired parameters may be estimated more accurately by sampling and averaging techniques over homogeneous subparts of the data base. (4 references) (Author)
Descriptors: Databases, Information Retrieval, Relevance (Information Retrieval)

Schlieder, Torsten; Meuss, Holger – Journal of the American Society for Information Science and Technology, 2002
Discussion of XML, information retrieval, precision, and recall focuses on a retrieval technique that adopts the similarity measure of the vector space model, incorporates the document structure, and supports structured queries. Topics include a query model based on tree matching; structured queries and term-based ranking; and term frequency and…
Descriptors: Information Retrieval, Models, Relevance (Information Retrieval)
Kemp, D. A. – Information Storage and Retrieval, 1974
The difference between pertinence and relevance is discussed. Other pairs of terms and the differences between their members are examined, and the suggestion is made that such studies could increase our understanding of the theory of information systems, and thence lead to practical improvements. (Author)
Descriptors: Information Retrieval, Information Systems, Relevance (Information Retrieval)

Sparck Jones, Karen – Journal of Documentation, 1975
It would be very helpful in retrieval experiments if good retrieval performance for a test collection was known, so that performance for particular devices could be fully evaluated. This paper presents one performance yardstick, based on optimally weighted request terms, and illustrates its application to different test collections. (Author)
Descriptors: Information Retrieval, Relevance (Information Retrieval), Research Design

Salton, G.; Waldstein, R. K. – Information Processing and Management, 1978
Term relevance weighting systems in interactive information retrieval are reviewed. An experiment in which information retrieval users ranked query terms in decreasing order of presumed importance prior to actual search and retrieval is described. (Author/KP)
Descriptors: Information Retrieval, Online Systems, Relevance (Information Retrieval)

Yates-Mercer, Penelope A. – Journal of Documentation, 1976
Farradane's relational indexing appeared applicable to the scientific area of the properties of metals and gave good results with a greater depth of indexing. (Author/PF)
Descriptors: Databases, Indexing, Information Retrieval, Relevance (Information Retrieval)

Belzer, Jack – Journal of the American Society for Information Science, 1973
Entropies of surrogates such as citations, abstracts, first paragraphs, last paragraphs, and first and last paragraphs are measures of how well each class of surrogates predicts relevancy of documents. They are measures of meaningful information in the text of surrogates. Such measures of information are important to information system designers.…
Descriptors: Information Retrieval, Information Theory, Relevance (Information Retrieval)
Heine, M. H. – Information Storage and Retrieval, 1973
The Marczewski-Steinhaus metric provides what appears to be an objective general measure of retrieval effectiveness within the framework of set theory and the theory of metric spaces. (19 references) (Author/SJ)
Descriptors: Information Retrieval, Relevance (Information Retrieval), Set Theory

Scheffler, Frederic; And Others – Journal of the American Society for Information Science, 1972
The use of Boolean not" logic in selective dissemination of information produced greater user satisfaction, less nonpertinent information, and no apparent decrease in the number of pertinent retrievals. (9 references) (SJ)
Descriptors: Information Dissemination, Information Retrieval, Relevance (Information Retrieval)
Foskett, D. J. – Information Storage and Retrieval, 1972
The author distinguished between relevance and pertinence as applied to information retrieval. (6 references) (SJ)
Descriptors: Information Needs, Information Retrieval, Relevance (Information Retrieval)
Swets, John A. – Amer Doc, 1969
Descriptors: Information Retrieval, Relevance (Information Retrieval), Statistical Analysis

Boughanem, M.; Christment, C.; Tamine, L. – Journal of the American Society for Information Science and Technology, 2002
Presents a genetic relevance optimization process performed in an information retrieval system that uses genetic techniques for solving multimodal problems (niching) and query reformulation techniques. Explains that the niching technique allows the process to reach different relevance regions of the document space, and that query reformulations…
Descriptors: Algorithms, Genetics, Information Retrieval, Relevance (Information Retrieval)