Keyphrase extraction based on topic relevance and term association

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Publication (help)
Keyphrase extraction based on topic relevance and term association
Authors: Decong Li, Sujian Li, Wenjie Li, Congyun Gu, Yun Li [edit item]
Citation: Journal of Information and Computational Science 7 (1): 293-299. 2010.
Publication type: Journal article
Peer-reviewed: Yes
Database(s):
DOI: Define doi.
Google Scholar cites: Not available
Link(s): Paper link
Added by Wikilit team: Yes
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Keyphrase extraction based on topic relevance and term association is a publication by Decong Li, Sujian Li, Wenjie Li, Congyun Gu, Yun Li.


[edit] Abstract

Keyphrases are concise representation of documents and usually are extracted directly from the original text. This paper proposes a novel approach to extract keyphrases. This method proposes two metrics, named topic relevance and term association respectively, for determining whether a term is a keyphrase. Using Wikipedia knowledge and betweenness computation, we compute these two metrics and combine them to extract important phrases from the text. Experimental results show the effectiveness of the proposed approach for keyphrases extaction.

[edit] Research questions

"In this paper, with the help of Wikipedia knowledge,we construct a semantic graph, based on which topic relevance and term associationq qre combined to extract keyphrases."

Research details

Topics: Semantic relatedness [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Mathematical modeling, Statistical analysis [edit item]
Data source: Wikipedia pages [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

"The keyphrase extraction approach proposed in this paper performs better in comparison to three other approaches."

[edit] Comments

"Wikipedia pages; documents

The Wikipedia language seems to be "English", but it seems not to be specified, so it should be "not specified"?

The study made an computer-based experiment to evaluate the performance of their keyphrase extraction method. if "computer-based experiment" is regarded as an "experiment" we should label this as an "experiment."


Further notes[edit]

Facts about "Keyphrase extraction based on topic relevance and term association"RDF feed
AbstractKeyphrases are concise representation of dKeyphrases are concise representation of documents and usually are extracted directly from the original text. This paper proposes a novel approach to extract keyphrases. This method proposes two metrics, named topic relevance and term association respectively, for determining whether a term is a keyphrase. Using Wikipedia knowledge and betweenness computation, we compute these two metrics and combine them to extract important phrases from the text. Experimental results show the effectiveness of the proposed approach for keyphrases extaction.roposed approach for keyphrases extaction.
Added by wikilit teamYes +
Collected data time dimensionCross-sectional +
CommentsWikipedia pages; documents

The Wikipedia Wikipedia pages; documents

The Wikipedia language seems to be "English", but it seems not to be specified, so it should be "not specified"?

The study made an computer-based experiment to evaluate the performance of their keyphrase extraction method. if "computer-based experiment" is regarded as an "experiment" we should label this as an "experiment.t" we should label this as an "experiment.
ConclusionThe keyphrase extraction approach proposed in this paper performs better in comparison to three other approaches.
Data sourceWikipedia pages +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Keyphrase%2Bextraction%2Bbased%2Bon%2Btopic%2Brelevance%2Band%2Bterm%2Bassociation%22 +
Has authorDecong Li +, Sujian Li +, Wenjie Li +, Congyun Gu + and Yun Li +
Has domainComputer science +
Has topicSemantic relatedness +
Issue1 +
Pages293-299 +
Peer reviewedYes +
Publication typeJournal article +
Published inJournal of Information and Computational Science +
Research designMathematical modeling + and Statistical analysis +
Research questionsIn this paper, with the help of Wikipedia knowledge,we construct a semantic graph, based on which topic relevance and term associationq qre combined to extract keyphrases.
Revid10,843 +
TheoriesUndetermined
Theory typeDesign and action +
TitleKeyphrase extraction based on topic relevance and term association
Unit of analysisArticle +
Urlhttp://www.joics.com/downloadpaper.aspx?id=120&name=2010_7_1_293_299.pdf +
Volume7 +
Wikipedia coverageSample data +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageEnglish +
Wikipedia page typeArticle +
Year2010 +