Difference between revisions of "Categorizing Learning Objects based on Wikipedia as Substitute Corpus"
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| + | | title = Categorizing Learning Objects based on Wikipedia as Substitute Corpus | ||
| + | | date = 2007 | ||
| + | | authors = [[Marek Meyer]]<br />[[Christoph Rensing]]<br />[[Ralf Steinmetz]] | ||
| + | | link = http://ceur-ws.org/Vol-311/paper09.pdf | ||
| + | | plink = https://www.researchgate.net/profile/Christoph_Rensing/publication/221549919_Categorizing_Learning_Objects_Based_On_Wikipedia_as_Substitute_Corpus/links/00b49525e388417e6c000000.pdf | ||
| + | }} | ||
'''Categorizing Learning Objects based on Wikipedia as Substitute Corpus''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Marek Meyer]], [[Christoph Rensing]] and [[Ralf Steinmetz]]. | '''Categorizing Learning Objects based on Wikipedia as Substitute Corpus''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Marek Meyer]], [[Christoph Rensing]] and [[Ralf Steinmetz]]. | ||
== Overview == | == Overview == | ||
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 [[categories]]. 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 Resources. Instead of using typical Learning Resources, the free encyclopedia [[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. | 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 [[categories]]. 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 Resources. Instead of using typical Learning Resources, the free encyclopedia [[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. | ||
Revision as of 09:20, 2 February 2021
Authors | Marek Meyer Christoph Rensing Ralf Steinmetz |
|---|---|
Publication date | 2007 |
Links | Original Preprint |
Categorizing Learning Objects based on Wikipedia as Substitute Corpus - scientific work related to Wikipedia quality published in 2007, written by Marek Meyer, Christoph Rensing and Ralf Steinmetz.
Overview
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 categories. 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 Resources. Instead of using typical Learning Resources, the free encyclopedia 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.
