Bibtex Record:
@workingpaper{
Author = {Frank, E.T. and Paynter, G.W.},
Title = {Predicting Library of Congress Classifications from Library of Congress Subject Headings},
Publisher = {Department of Computer Science, The University of Waikato},
Number = {01/03},
Pages = {1-23},
Month = {January},
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.},
Year = {2003}
}