Bibliographic Details
| Title: |
Predicting Library of Congress Classifications From Library of Congress Subject Headings. |
| Authors: |
Frank, Eibe1 eibe@cs.waikato.ac.nz, Paynter, Gordon W.2 gordon.paynter@ucr.edu |
| Source: |
Journal of the American Society for Information Science & Technology. Feb2004, Vol. 55 Issue 3, p214-227. 14p. |
| Subjects: |
Library of Congress classification, Library of Congress subject headings, Catalogs, Classification, Subject headings, Subject cataloging |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
