INCLUSION OF RELEVANCE INFORMATION IN THE TERM DISCRIMINATION MODEL

INCLUSION OF RELEVANCE INFORMATION IN THE TERM DISCRIMINATION MODEL The term discrimination value of an index term has been proposed as a quantitative measure of the extent to which that term can discriminate between documents in bibliographic databases. Previous work has suggested that the most discriminating terms are those with medium frequencies of occurrence. This paper discusses the effect of including relevance data on the calculation of term discrimination values. Two algorithms are described that calculate the ability of index terms to discriminate between relevant documents, between nonrelevant documents or between relevant and nonrelevant documents. The application of these algorithms to several standard document test collections demonstrates that the exact form of the relationship between term frequency and term discrimination depends upon the particular type of discrimination which is being measured in particular, medium frequency terms are not necessarily the best discriminators when relevance data is available. These results are compared with the discriminatory ability of terms as measured by their relevance weights, where the most discriminating terms are those with low frequencies of occurrence. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Documentation Emerald Publishing

INCLUSION OF RELEVANCE INFORMATION IN THE TERM DISCRIMINATION MODEL

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0022-0418
DOI
10.1108/eb026840
Publisher site
See Article on Publisher Site

Abstract

The term discrimination value of an index term has been proposed as a quantitative measure of the extent to which that term can discriminate between documents in bibliographic databases. Previous work has suggested that the most discriminating terms are those with medium frequencies of occurrence. This paper discusses the effect of including relevance data on the calculation of term discrimination values. Two algorithms are described that calculate the ability of index terms to discriminate between relevant documents, between nonrelevant documents or between relevant and nonrelevant documents. The application of these algorithms to several standard document test collections demonstrates that the exact form of the relationship between term frequency and term discrimination depends upon the particular type of discrimination which is being measured in particular, medium frequency terms are not necessarily the best discriminators when relevance data is available. These results are compared with the discriminatory ability of terms as measured by their relevance weights, where the most discriminating terms are those with low frequencies of occurrence.

Journal

Journal of DocumentationEmerald Publishing

Published: Feb 1, 1989

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