Predicting Library of Congress classifications from Library of Congress subject headings

שמור ב:

מידע ביבליוגרפי
הוצא לאור ב:	Journal of the American Society for Information Science and Technology vol. 55, no. 3 (Feb 1, 2004), p. 214
מחבר ראשי:	Frank, Eibe
מחברים אחרים:	Paynter, Gordon W
יצא לאור:	Wiley Periodicals Inc.
נושאים:	Library of Congress Springer Verlag California New York San Francisco California United States > US Studies Libraries Library cataloging Classification Machine learning Maps Topics Metadata Science Subject heading schemes Library of Congress Subject Headings Library catalogs Librarians Access to information Internet resources Information science Digital libraries Subject headings Legislatures Hierarchies Academic Libraries Electronic Libraries Learning Problems Information Retrieval Cataloging Teaching Methods Academic Achievement Diagnostic Teaching Catalogs Artificial Intelligence Library Personnel
גישה מקוונת:	Citation/Abstract Full Text - PDF
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תיאור
Resumen:	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. [PUBLICATION ABSTRACT]
ISSN:	1532-2882 1532-2890 2330-1635 2330-1643 0096-946X 0002-8231
DOI:	10.1002/asi.10360
Fuente:	ABI/INFORM Global