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This article presents the results of research into the au tomatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records. The method used in this study was based on partial match retrieval techniques using vari ous elements of new records (i.e., those to be classi fied) as "queries," and a test database of classification clusters generated from previously classified MARC records. Sixty individual methods for automatic classifi cation were tested on a set of 283 new records, using all combinations of four different partial match methods, five query types, and three representations of search terms. The results indicate that if the best method for a particular case can be determined, then up to 86% of the new records may be correctly classified. The single method with the best accuracy was able to select the correct classification for about 46% of the new records.
Details
Automatic classification;
Colon classification;
Classification;
Science;
Query expansion;
Technical services;
Subject heading schemes;
Library of Congress Classification;
Library resources;
Classification schemes;
Relevance feedback;
Titles;
Dewey Decimal Classification;
Library of Congress Subject Headings;
Information retrieval;
Retrieval performance measures;
Information science;
Library collections;
Clustering;
Experiments;
Methods;
Algorithms;
Queries;
Documents;
Databases;
Subject headings;
Archives & records;
Retrieval;
Legislatures;
Libraries