Experiments in Automatic Library of Congress Classification
Experiments in Automatic Library of Congress Classification
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国会图书馆自动分类实验
DOI：
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发表时间：
1992
期刊：
影响因子：
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通讯作者：
R. Larson
中科院分区：
文献类型：
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作者：
R. Larson
本文介绍了根据MARC著录中的题名和主题词自动选择美国国会图书馆分类号的研究成果。这项研究中使用的方法是基于部分匹配检索技术，使用新记录的各种元素(即，要分类的记录)作为“查询”，以及从先前分类的MARC记录生成的分类簇的测试数据库。在一组283条新记录上，使用四种不同的部分匹配方法、五种查询类型和三种搜索词表示的所有组合，对60种单独的自动分类方法进行了测试。结果表明，如果能够确定针对特定情况的最佳方法，则高达86%的新记录可以被正确分类。准确度最高的单一方法能够为大约46%的新记录选择正确的分类。©1992 John Wiley父子公司
This article presents the results of research into the automatic 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 various elements of new records (i.e., those to be classified) as “queries,” and a test database of classification clusters generated from previously classified MARC records. Sixty individual methods for automatic classification 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. © 1992 John Wiley & Sons, Inc.