Wikify!: linking documents to encyclopedic knowledge

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Wikify!: linking documents to encyclopedic knowledge
Authors: Rada Mihalcea, Andras Csomai [edit item]
Citation: CIKM '07 Proceedings of the sixteenth ACM conference on Conference on information and knowledge management  : 233-242. 2007.
Publication type: Conference paper
Peer-reviewed: Yes
Database(s):
DOI: 10.1145/1321440.1321475.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Wikify!: linking documents to encyclopedic knowledge is a publication by Rada Mihalcea, Andras Csomai.


[edit] Abstract

This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks. The paper also shows how the two methods can be combined into a system able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages. Evaluations of the system show that the automatic annotations are reliable and hardly distinguishable from manual annotations.

[edit] Research questions

"This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks. The paper also shows how the two methods can be combined into a system able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages."

Research details

Topics: Information extraction [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Main topic [edit item]
Theories: "Undetermined" [edit item]
Research design: Grounded theory [edit item]
Data source: Interview responses [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Live Wikipedia [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English [edit item]

[edit] Conclusion

"Through independent evaluations carried out for each of the two tasks, we showed that both the keyword extraction and the word sense disambiguation systems produce accurate annotations, with performance figures significantly higher than competitive baselines. We also performed an overall evaluation of the Wikify! system using a Turing-like test, which showed that the output of the Wikify! system was hardly distinguishable from the manual annotations produced by Wikipedia contributors."

[edit] Comments

""Evaluations of the system show that the automatic annotations are reliable and hardly distinguishable from manual annotations." p. 233"


Further notes[edit]

Facts about "Wikify!: linking documents to encyclopedic knowledge"RDF feed
AbstractThis paper introduces the use of WikipediaThis paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks. The paper also shows how the two methods can be combined into a system able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages. Evaluations of the system show that the automatic annotations are reliable and hardly distinguishable from manual annotations.y distinguishable from manual annotations.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
Comments"Evaluations of the system show that the automatic annotations are reliable and hardly distinguishable from manual annotations." p. 233
ConclusionThrough independent evaluations carried ouThrough independent evaluations carried out for each of

the two tasks, we showed that both the keyword extraction and the word sense disambiguation systems produce accurate annotations, with performance figures significantly higher than competitive baselines. We also performed an overall evaluation of the Wikify! system using a Turing-like test, which showed that the output of the Wikify! system

was hardly distinguishable from the manual annotations produced by Wikipedia contributors.
ations produced by Wikipedia contributors.
Data sourceInterview responses +
Doi10.1145/1321440.1321475 +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Wikify%21%3A%2Blinking%2Bdocuments%2Bto%2Bencyclopedic%2Bknowledge%22 +
Has authorRada Mihalcea + and Andras Csomai +
Has domainComputer science +
Has topicInformation extraction +
Pages233-242 +
Peer reviewedYes +
Publication typeConference paper +
Published inCIKM '07 Proceedings of the sixteenth ACM conference on Conference on information and knowledge management +
Research designGrounded theory +
Research questionsThis paper introduces the use of WikipediaThis paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks. The paper also shows how the two methods can be combined into a system able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages.epts to the corresponding Wikipedia pages.
Revid11,068 +
TheoriesUndetermined
Theory typeDesign and action +
TitleWikify!: linking documents to encyclopedic knowledge
Unit of analysisArticle +
Urlhttp://dl.acm.org/citation.cfm?id=1321440.1321475 +
Wikipedia coverageMain topic +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageEnglish +
Wikipedia page typeArticle +
Year2007 +