Categorizing Learning Objects Based On Wikipedia as Substitute Corpus
Key: MRS07-3
Author: Marek Meyer, Christoph Rensing, Ralf Steinmetz
Date: September 2007
Kind: In proceedings
Book title: Proceedings of the First International Workshop on Learning Object Discovery & Exchange (LODE'07)
Abstract: As 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.
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