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Experiments in Automatic Library of Congress Classification.

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Title: Experiments in Automatic Library of Congress Classification.
Authors: Larson, Ray R.1
Source: Journal of the American Society for Information Science. Mar1992, Vol. 43 Issue 2, p130-148. 19p.
Subjects: Automatic classification, Automatic indexing, Information storage & retrieval systems, Online library catalogs, MARC formats, Database searching
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Society for Information Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Experiments in Automatic Library of Congress Classification.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science%22">Journal of the American Society for Information Science</searchLink>. Mar1992, Vol. 43 Issue 2, p130-148. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Automatic+classification%22">Automatic classification</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+indexing%22">Automatic indexing</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22Online+library+catalogs%22">Online library catalogs</searchLink><br /><searchLink fieldCode="DE" term="%22MARC+formats%22">MARC formats</searchLink><br /><searchLink fieldCode="DE" term="%22Database+searching%22">Database searching</searchLink>
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Society for Information Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/(SICI)1097-4571(199203)43:2<130::AID-ASI3>3.0.CO;2-S
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      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Automatic classification
        Type: general
      – SubjectFull: Automatic indexing
        Type: general
      – SubjectFull: Information storage & retrieval systems
        Type: general
      – SubjectFull: Online library catalogs
        Type: general
      – SubjectFull: MARC formats
        Type: general
      – SubjectFull: Database searching
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      – TitleFull: Experiments in Automatic Library of Congress Classification.
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              Text: Mar1992
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              Y: 1992
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