Predicting Library of Congress classifications from Library of Congress subject headings

Eibe Frank and Gordon W. Paynter

Journal of the American Society for Information Science and Technology, 2004, vol. 55, issue 3, 214-227

Abstract: This paper addresses the problem of automatically assigning a Library of Congress Classification (LCC) to a work given its set of Library of Congress Subject Headings (LCSH). LCCs are organized in a tree: The root node of this hierarchy comprises all possible topics, and leaf nodes correspond to the most specialized topic areas defined. We describe a procedure that, given a resource identified by its LCSH, automatically places that resource in the LCC hierarchy. The procedure uses machine learning techniques and training data from a large library catalog to learn a model that maps from sets of LCSH to classifications from the LCC tree. We present empirical results for our technique showing its accuracy on an independent collection of 50,000 LCSH/LCC pairs.

Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:55:y:2004:i:3:p:214-227

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