Difference between revisions of "Conceptualizing Documents with Wikipedia"
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| + | | title = Conceptualizing Documents with Wikipedia | ||
| + | | date = 2012 | ||
| + | | authors = [[Tadashi Nomoto]]<br />[[Noriko Kando]] | ||
| + | | doi = 10.1145/2390148.2390155 | ||
| + | | link = http://dl.acm.org/citation.cfm?doid=2390148.2390155 | ||
| + | }} | ||
'''Conceptualizing Documents with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Tadashi Nomoto]] and [[Noriko Kando]]. | '''Conceptualizing Documents with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Tadashi Nomoto]] and [[Noriko Kando]]. | ||
== Overview == | == Overview == | ||
In this work, authors will discuss how to improve Wikilabel, an approach which makes use of titles in [[Wikipedia]] pages to generate labels for documents, by retooling ideas from story link detection (SLD). A comparison of approach against Elastic Net, a powerful machine learner, on the real world data, finds the visible superiority of approach over the latter. | In this work, authors will discuss how to improve Wikilabel, an approach which makes use of titles in [[Wikipedia]] pages to generate labels for documents, by retooling ideas from story link detection (SLD). A comparison of approach against Elastic Net, a powerful machine learner, on the real world data, finds the visible superiority of approach over the latter. | ||
Revision as of 09:50, 16 December 2019
Authors | Tadashi Nomoto Noriko Kando |
|---|---|
Publication date | 2012 |
DOI | 10.1145/2390148.2390155 |
Links | Original |
Conceptualizing Documents with Wikipedia - scientific work related to Wikipedia quality published in 2012, written by Tadashi Nomoto and Noriko Kando.
Overview
In this work, authors will discuss how to improve Wikilabel, an approach which makes use of titles in Wikipedia pages to generate labels for documents, by retooling ideas from story link detection (SLD). A comparison of approach against Elastic Net, a powerful machine learner, on the real world data, finds the visible superiority of approach over the latter.
