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Automatic Identification of Tag Types in a Resource- Based Learning Scenario

Key:BLRS11
Author:Doreen Böhnstedt, Lasse Lehmann, Christoph Rensing, Ralf Steinmetz
Date:September 2011
Kind:In proceedings - use for conference & workshop papers
Publisher:Springer
Address:Heidelberg
Book title:Towards Ubiquitious Learning, Proceedings of the 6th European Conference on Technology Enhanced Learning, EC-TEL 2011
Editor:Carlos Delgado Kloos, Denis Gillet, Raquel M. Crespo Garcia, Fridolin Wild, Martin Wolpers
Number:LNCS 6964
Pages:57-70
ISBN:9783642239847
Language:English
Keywords:Tagging, Tag Type Identification, Semantic Tagging
Number of characters:37560
Research Area(s):Knowledge Media
Abstract:When users use tags they often have a rich semantic structure in mind, which can not be fully explicated using existing tagging systems. However, a tagging system needs to be simple in order to be successful, otherwise it will not be accepted by users. In our ELWMS.KOM system for the support of self-regulated Resource-Based Learning users can assign specific semantic types to the tags they use in order to manage their web-based learning resources. However studies have shown that most users would appreciate an automatic identification of tag types. In this paper we present a knowledgebased approach for the automatic identification of the tag types used in the ELWMS.KOM system. Evaluations conducted on different corpora show that the algorithm works with an overall accuracy of up to 84%.
URL:http://www.springerlink.com/content/l644613v5722u807/fulltext.pdf

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Prof. Dr.-Ing. Ralf Steinmetz

Technische Universität Darmstadt
Fachgebiet Multimedia Kommunikation
Rundeturmstr. 10
64283 Darmstadt
S3/20

+49 6151 16-6150

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