Document Type

Conference Paper

Journal/Book Title/Conference

Hawaii International Conference on System Sciences

Publication Date

2007

First Page

73b

Last Page

73b

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.

Comments

Originally published by the Institute of Electrical and Electronics Engineers. Available online through the Hawaii International Conference on System Sciences.

DOI

10.1109/HICSS.2007.517

 
 

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