Wikirelate! Computing semantic relatedness using Wikipedia

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Wikirelate! Computing semantic relatedness using Wikipedia
Authors: Michael Strube, Simone Paolo Ponzetto [edit item]
Citation: AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2  : 1419-1424. 2006.
Publication type: Conference paper
Peer-reviewed: Yes
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Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Wikirelate! Computing semantic relatedness using Wikipedia is a publication by Michael Strube, Simone Paolo Ponzetto.


[edit] Abstract

Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.

[edit] Research questions

"Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets."

Research details

Topics: Semantic relatedness [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Case [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: Experiment responses, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Dump [edit item]
Wikipedia page type: Article, Information categorization and navigation [edit item]
Wikipedia language: English [edit item]

[edit] Conclusion

"Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts."

[edit] Comments

""Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose." p. 1419"


Further notes[edit]

Facts about "Wikirelate! Computing semantic relatedness using Wikipedia"RDF feed
AbstractWikipedia provides a knowledge base for coWikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.tion processing naturally occurring texts.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
Comments"Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose." p. 1419
ConclusionExisting relatedness measures perform bettExisting relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.tion processing naturally occurring texts.
Data sourceExperiment responses + and Wikipedia pages +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Wikirelate%21%2BComputing%2Bsemantic%2Brelatedness%2Busing%2BWikipedia%22 +
Has authorMichael Strube + and Simone Paolo Ponzetto +
Has domainComputer science +
Has topicSemantic relatedness +
Pages1419-1424 +
Peer reviewedYes +
Publication typeConference paper +
Published inAAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2 +
Research designExperiment +
Research questionsWikipedia provides a knowledge base for coWikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. WordNet on various benchmarking datasets.
Revid11,104 +
TheoriesUndetermined
Theory typeDesign and action +
TitleWikirelate! Computing semantic relatedness using Wikipedia
Unit of analysisArticle +
Urlhttp://www.aaai.org/Papers/AAAI/2006/AAAI06-223.pdf +
Wikipedia coverageCase +
Wikipedia data extractionDump +
Wikipedia languageEnglish +
Wikipedia page typeArticle + and Information categorization and navigation +
Year2006 +