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Categorizing Learning Objects Based On Wikipedia as Substitute Corpus
Abstract As metadata is often not sufficiently provAs metadata is often not sufficiently provided by authors of Learning Resources, automatic metadata generation methods are used to create metadata afterwards. One kind of metadata is categorization, particularly the partition of Learning Resources into distinct subject cat- egories. A disadvantage of state-of-the-art categorization methods is that they require corpora of sample Learning Resources. Unfortunately, large corpora of well-labeled Learning Resources are rare. This paper presents a new approach for the task of subject categorization of Learning Re- sources. Instead of using typical Learning Resources, the free encyclope- dia Wikipedia is applied as training corpus. The approach presented in this paper is to apply the k-Nearest-Neighbors method for comparing a Learning Resource to Wikipedia articles. Different parameters have been evaluated regarding their impact on the categorization performance. impact on the categorization performance.
Abstractsub As metadata is often not sufficiently provAs metadata is often not sufficiently provided by authors of Learning Resources, automatic metadata generation methods are used to create metadata afterwards. One kind of metadata is categorization, particularly the partition of Learning Resources into distinct subject cat- egories. A disadvantage of state-of-the-art categorization methods is that they require corpora of sample Learning Resources. Unfortunately, large corpora of well-labeled Learning Resources are rare. This paper presents a new approach for the task of subject categorization of Learning Re- sources. Instead of using typical Learning Resources, the free encyclope- dia Wikipedia is applied as training corpus. The approach presented in this paper is to apply the k-Nearest-Neighbors method for comparing a Learning Resource to Wikipedia articles. Different parameters have been evaluated regarding their impact on the categorization performance. impact on the categorization performance.
Bibtextype inproceedings  +
Has author Marek Meyer + , Christoph Rensing + , Ralf Steinmetz +
Has keyword Wikipedia + , Categorization + , Metadata + , KNN + , Classification + , Substitute Corpus + , Automatic Metadata Generation +
Has remote mirror http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-311/paper09.pdf  +
Number of citations by publication 1  +
Number of references by publication 0  +
Published in First International Workshop on Learning Object Discovery & Exchange (LODE'07), September 18, 2007, Crete, Greece +
Title Categorizing Learning Objects Based On Wikipedia as Substitute Corpus +
Type conference paper  +
Year 2007 +
Creation dateThis property is a special property in this wiki. 20 September 2014 17:55:12  +
Categories Publications without language parameter  + , Publications without license parameter  + , Publications without DOI parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 20 September 2014 17:55:12  +
DateThis property is a special property in this wiki. 2007  +
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