Categorizing Learning Objects Based On Wikipedia as Substitute Corpus
|Categorizing Learning Objects Based On Wikipedia as Substitute Corpus|
|Author(s)||Marek Meyer, Christoph Rensing, Ralf Steinmetz|
|Published in||First International Workshop on Learning Object Discovery & Exchange (LODE'07), September 18, 2007, Crete, Greece|
|Keyword(s)||Wikipedia, Categorization, Metadata, kNN, Classification, Substitute Corpus, Automatic Metadata Generation|
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Categorizing Learning Objects Based On Wikipedia as Substitute Corpus is a 2007 conference paper by Marek Meyer, Christoph Rensing, Ralf Steinmetz and published in First International Workshop on Learning Object Discovery & Exchange (LODE'07), September 18, 2007, Crete, Greece.
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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Usage of Wikipedia as corpus for machine learning methods.