Standards alignment for metadata assignment

Authors:
Anne R. Diekema;Ozgur Yilmazel;Jennifer Bailey;Sarah C. Harwell;Elizabeth D. Liddy
Affiliations:
Center for Natural Language Processing, Syracuse, NY;Center for Natural Language Processing, Syracuse, NY;Center for Natural Language Processing, Syracuse, NY;Center for Natural Language Processing, Syracuse, NY;Center for Natural Language Processing, Syracuse, NY
Venue:
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Year:
2007

Citing 4
Cited 2

An Evaluation of Statistical Approaches to Text Categorization

Information Retrieval
Hierarchically Classifying Documents Using Very Few Words

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining machine learning and hierarchical structures for text categorization

Combining machine learning and hierarchical structures for text categorization
Empirical selection of nlp-driven document representations for text categorization

Empirical selection of nlp-driven document representations for text categorization

Computer-assisted assignment of educational standards using natural language processing

Journal of the American Society for Information Science and Technology
Can intermediary-based science standards crosswalking work? Some evidence from mining the standard alignment tool (SAT)

Journal of the American Society for Information Science and Technology

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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.