Imagen de Portada

Predicting Library of Congress classifications from Library of Congress subject headings

Guardado en:
Publicado en:Journal of the American Society for Information Science and Technology vol. 55, no. 3 (Feb 1, 2004), p. 214
Autor principal: Frank, Eibe
Otros Autores: Paynter, Gordon W
Publicado:
Wiley Periodicals Inc.
Materias:
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
Acceso en línea:Citation/Abstract
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
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