Frank, EibePaynter, Gordon W.2024-01-162024-01-162004-02-011532-2882https://hdl.handle.net/10289/16340This 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). 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 model which 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.application/pdfEnglishThis is an author’s accepted version of a article published in Journal of the American Society for Information Science and Technology. © 2003 Wiley.Science & TechnologyTechnologyComputer Science, Information SystemsInformation Science & Library ScienceComputer ScienceJournal Article10.1002/asi.103601532-2890