Standards alignment for metadata assignment

Anne R. Diekema, Ozgur Yilmazel, Jennifer Bailey, Sarah C. Harwell, Elizabeth D. Liddy

Research output: Chapter in Book/Entry/PoemConference contribution

8 Scopus citations

Abstract

The research in this paper describes a Machine Learning technique called hierarchical text categorization which is used to solve the problem of finding equivalents from among different state and national education standards. The approach is based on a set of manually aligned standards and utilizes the hierarchical structure present in the standards to achieve a more accurate result. Details of this approach and its evaluation are presented.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2007
Subtitle of host publicationBuilding and Sustaining the Digital Environment
Pages398-399
Number of pages2
DOIs
StatePublished - 2007
Event7th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2007: Building and Sustaining the Digital Environment - Vancouver, BC, Canada
Duration: Jun 18 2007Jun 23 2007

Publication series

NameProceedings of the ACM International Conference on Digital Libraries

Other

Other7th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2007: Building and Sustaining the Digital Environment
Country/TerritoryCanada
CityVancouver, BC
Period6/18/076/23/07

Keywords

  • Automatic metadata assignment
  • Educational standards
  • Hierarchical text classification
  • Machine learning
  • NSDL
  • Natural language processing

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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