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Keyphrase extraction based on topic relevance and term association
Abstract Keyphrases 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 team Yes  +
Collected data time dimension Cross-sectional  +
Comments Wikipedia 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.
Conclusion The keyphrase extraction approach proposed in this paper performs better in comparison to three other approaches.
Data source Wikipedia pages  +
Google scholar url  +
Has author Decong Li + , Sujian Li + , Wenjie Li + , Congyun Gu + , Yun Li +
Has domain Computer science +
Has topic Semantic relatedness +
Issue 1  +
Pages 293-299  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in Journal of Information and Computational Science +
Research design Mathematical modeling  + , Statistical analysis  +
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.
Revid 10,843  +
Theories Undetermined
Theory type Design and action  +
Title Keyphrase extraction based on topic relevance and term association
Unit of analysis Article  +
Url  +
Volume 7  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Live Wikipedia  +
Wikipedia language English  +
Wikipedia page type Article  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:29:25  +
Categories Publications with missing gscites  + , Semantic relatedness  + , Computer science  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:29:21  +
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