Text categorization for aligning educational standards

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Standard alignment (where standards describing similar concepts are correlated) is a necessary task in providing full access to educational resources. Manual alignment is time consuming and expensive. We propose an automatic alignment system, using machine learning techniques utilizing natural language processing. In this paper we discuss our experiments on text categorization for automatic alignment. We explore the role of relevant vocabulary sets in automatic alignment.

Original languageEnglish (US)
Title of host publicationProceedings of the 40th Annual Hawaii International Conference on System Sciences 2007, HICSS'07
DOIs
StatePublished - 2007
Event40th Annual Hawaii International Conference on System Sciences 2007, HICSS'07 - Big Island, HI, United States
Duration: Jan 3 2007Jan 6 2007

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Other

Other40th Annual Hawaii International Conference on System Sciences 2007, HICSS'07
CountryUnited States
CityBig Island, HI
Period1/3/071/6/07

ASJC Scopus subject areas

  • Engineering(all)

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