Predicting library of congress classifications from library of congress subject headings

This paper addresses the problem of automatically as-signing a Library of Congress Classification (LCC) to a work given its set of Library of Congress Subject Head-ings (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 re-source in the LCC hierarchy. The procedure uses ma-chine 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