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      • Computing and Mathematical Sciences
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      • 2003 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2003 Working Papers
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      Predicting Library of Congress Classifications from Library of Congress Subject Headings

      Frank, Eibe; Paynter, Gordon W.
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      Frank, E. & Paynter, G. (2003). Predicting Library of Congress Classifications from Library of Congress Subject Headings. (Working paper 01/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1011
      Abstract
      This paper addresses the problem of automatically assigning a Library of Congress Classification (LCC) to work given its set of Library of Congress Subject Headings (LCSH). LCC 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 classification model mapping from sets of LCSH to nodes in the LCC tree. We present empirical results for our technique showing its accuracy on an independent collection of 50,000 LCSH/LCC pairs.
      Date
      2003-01
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      01/03
      Publisher
      University of Waikato
      Collections
      • 2003 Working Papers [8]
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