Difference between revisions of "Topic Identification Using Wikipedia Graph Centrality"
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| + | | title = Topic Identification Using Wikipedia Graph Centrality | ||
| + | | date = 2009 | ||
| + | | authors = [[Kino Coursey]]<br />[[Rada Mihalcea]] | ||
| + | | doi = 10.3115/1620853.1620887 | ||
| + | | link = http://dl.acm.org/citation.cfm?id=1620853.1620887 | ||
| + | }} | ||
'''Topic Identification Using Wikipedia Graph Centrality''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Kino Coursey]] and [[Rada Mihalcea]]. | '''Topic Identification Using Wikipedia Graph Centrality''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Kino Coursey]] and [[Rada Mihalcea]]. | ||
== Overview == | == Overview == | ||
This paper presents a method for automatic topic identification using a graph-centrality algorithm applied to an encyclopedic graph derived from [[Wikipedia]]. When tested on a data set with manually assigned topics, the system is found to significantly improve over a simpler baseline that does not make use of the external encyclopedic knowledge. | This paper presents a method for automatic topic identification using a graph-centrality algorithm applied to an encyclopedic graph derived from [[Wikipedia]]. When tested on a data set with manually assigned topics, the system is found to significantly improve over a simpler baseline that does not make use of the external encyclopedic knowledge. | ||
Revision as of 10:20, 3 July 2020
Authors | Kino Coursey Rada Mihalcea |
|---|---|
Publication date | 2009 |
DOI | 10.3115/1620853.1620887 |
Links | Original |
Topic Identification Using Wikipedia Graph Centrality - scientific work related to Wikipedia quality published in 2009, written by Kino Coursey and Rada Mihalcea.
Overview
This paper presents a method for automatic topic identification using a graph-centrality algorithm applied to an encyclopedic graph derived from Wikipedia. When tested on a data set with manually assigned topics, the system is found to significantly improve over a simpler baseline that does not make use of the external encyclopedic knowledge.
