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Predicting Library of Congress classifications from Library of Congress subject headings

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  • Eibe Frank
  • Gordon W. Paynter

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.

Suggested Citation

  • Eibe Frank & Gordon W. Paynter, 2004. "Predicting Library of Congress classifications from Library of Congress subject headings," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(3), pages 214-227, February.
    • Handle: RePEc:bla:jamist:v:55:y:2004:i:3:p:214-227
    DOI: 10.1002/asi.10360
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    Cited by:

    1. Charles-Antoine Julien & Pierre Tirilly & John E. Leide & Catherine Guastavino, 2012. "Constructing a true LCSH tree of a science and engineering collection," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2405-2418, December.

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