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  • × author_ss:"Paynter, G.W."
  1. Frank, E.; Paynter, G.W.: Predicting Library of Congress Classifications from Library of Congress Subject Headings (2004) 0.01 
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    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 an an independent collection of 50,000 LCSH/LCC pairs.
  2. Jones, S.; Paynter, G.W.: Automatic extractionof document keyphrases for use in digital libraries : evaluations and applications (2002) 0.01 
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  3. Nichols, D.M.; Paynter, G.W.; Chan, C.-H.; Bainbridge, D.; McKay, D.; Twidale, M.B.; Blandford, A.: Experiences in deploying metadata analysis tools for institutional repositories (2009) 0.00 
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    Abstract
    Current institutional repository software provides few tools to help metadata librarians understand and analyse their collections. In this paper, we compare and contrast metadata analysis tools that were developed simultaneously, but independently, at two New Zealand institutions during a period of national investment in research repositories: the Metadata Analysis Tool (MAT) at The University of Waikato, and the Kiwi Research Information Service (KRIS) at the National Library of New Zealand. The tools have many similarities: they are convenient, online, on-demand services that harvest metadata using OAI-PMH, they were developed in response to feedback from repository administrators, and they both help pinpoint specific metadata errors as well as generating summary statistics. They also have significant differences: one is a dedicated tool while the other is part of a wider access tool; one gives a holistic view of the metadata while the other looks for specific problems; one seeks patterns in the data values while the other checks that those values conform to metadata standards. Both tools work in a complementary manner to existing web-based administration tools. We have observed that discovery and correction of metadata errors can be quickly achieved by switching web browser views from the analysis tool to the repository interface, and back. We summarise the findings from both tools' deployment into a checklist of requirements for metadata analysis tools.

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