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Predicting Library of Congress Classifications From Library of Congress Subject Headings.

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Title: Predicting Library of Congress Classifications From Library of Congress Subject Headings.
Authors: Frank, Eibe1 eibe@cs.waikato.ac.nz, Paynter, Gordon W.2 gordon.paynter@ucr.edu
Source: Journal of the American Society for Information Science & Technology. Feb2004, Vol. 55 Issue 3, p214-227. 14p.
Subjects: Library of Congress classification, Library of Congress subject headings, Catalogs, Classification, Subject headings, Subject cataloging
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Society for Information Science & Technology 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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  Data: <searchLink fieldCode="AR" term="%22Frank%2C+Eibe%22">Frank, Eibe</searchLink><relatesTo>1</relatesTo><i> eibe@cs.waikato.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Paynter%2C+Gordon+W%2E%22">Paynter, Gordon W.</searchLink><relatesTo>2</relatesTo><i> gordon.paynter@ucr.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Feb2004, Vol. 55 Issue 3, p214-227. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Library+of+Congress+classification%22">Library of Congress classification</searchLink><br /><searchLink fieldCode="DE" term="%22Library+of+Congress+subject+headings%22">Library of Congress subject headings</searchLink><br /><searchLink fieldCode="DE" term="%22Catalogs%22">Catalogs</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Subject+headings%22">Subject headings</searchLink><br /><searchLink fieldCode="DE" term="%22Subject+cataloging%22">Subject cataloging</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Society for Information Science & Technology 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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