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Standards alignment for metadata assignment

Published:18 June 2007Publication History

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.

References

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  2. Koller, D. and Sahami, M. Hierarchically classifying documents using very few words. 14th International Conference on Machine Learning, Nashville, TN, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. McREL. The McREL home page. Available: http://www.mcrel.org/topics/Standards/. Accessed 5 February 2007. Last updated: unknown.Google ScholarGoogle Scholar
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  5. Ruiz, M. E. Combining machine learning and hierarchical structures for text categorization. PhD. Thesis. December 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Yang, Y. An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval, Vol. 1, 69--90, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Yilmazel, O. Empirical Selection of NLP-Driven Document Representations for Text Categorization. Doctoral Dissertation. Syracuse University, 2006. 103p. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Standards alignment for metadata assignment

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        • Published in

          cover image ACM Conferences
          JCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
          June 2007
          534 pages
          ISBN:9781595936448
          DOI:10.1145/1255175

          Copyright © 2007 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 18 June 2007

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          Overall Acceptance Rate415of1,482submissions,28%

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