Difference between revisions of "Improving Keyphrase Extraction Using Wikipedia Semantics"

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{{Infobox work
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| title = Improving Keyphrase Extraction Using Wikipedia Semantics
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| date = 2008
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| authors = [[Tianyi Shi]]<br />[[Shidou Jiao]]<br />[[Junqi Hou]]<br />[[Minglu Li]]
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| doi = 10.1109/IITA.2008.211
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| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4739723
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}}
 
'''Improving Keyphrase Extraction Using Wikipedia Semantics''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Tianyi Shi]], [[Shidou Jiao]], [[Junqi Hou]] and [[Minglu Li]].
 
'''Improving Keyphrase Extraction Using Wikipedia Semantics''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Tianyi Shi]], [[Shidou Jiao]], [[Junqi Hou]] and [[Minglu Li]].
  
 
== Overview ==
 
== Overview ==
 
Keyphrase extraction plays a key role in various fields such as [[information retrieval]], text classification etc. However, most traditional keyphrase extraction methods relies on word frequency and position instead of document inherent [[semantic information]], often results in inaccurate output. In this paper, authors propose a novel automatic keyphrase extraction algorithm using semantic [[features]] mined from online [[Wikipedia]]. This algorithm first identifies candidate keyphrases based on lexical methods, and then a semantic graph which connects candidate keyphrases with document topics is constructed. Afterwards, a link analysis algorithm is applied to assign semantic feature weight to the candidate keyphrases. Finally, several statistical and semantic features are assembled by a regression model to predict the quality of candidates. Encouraging results are achieved in experiments which show the effectiveness of method.
 
Keyphrase extraction plays a key role in various fields such as [[information retrieval]], text classification etc. However, most traditional keyphrase extraction methods relies on word frequency and position instead of document inherent [[semantic information]], often results in inaccurate output. In this paper, authors propose a novel automatic keyphrase extraction algorithm using semantic [[features]] mined from online [[Wikipedia]]. This algorithm first identifies candidate keyphrases based on lexical methods, and then a semantic graph which connects candidate keyphrases with document topics is constructed. Afterwards, a link analysis algorithm is applied to assign semantic feature weight to the candidate keyphrases. Finally, several statistical and semantic features are assembled by a regression model to predict the quality of candidates. Encouraging results are achieved in experiments which show the effectiveness of method.

Latest revision as of 09:15, 11 February 2020


Improving Keyphrase Extraction Using Wikipedia Semantics
Authors
Tianyi Shi
Shidou Jiao
Junqi Hou
Minglu Li
Publication date
2008
DOI
10.1109/IITA.2008.211
Links
Original

Improving Keyphrase Extraction Using Wikipedia Semantics - scientific work related to Wikipedia quality published in 2008, written by Tianyi Shi, Shidou Jiao, Junqi Hou and Minglu Li.

Overview

Keyphrase extraction plays a key role in various fields such as information retrieval, text classification etc. However, most traditional keyphrase extraction methods relies on word frequency and position instead of document inherent semantic information, often results in inaccurate output. In this paper, authors propose a novel automatic keyphrase extraction algorithm using semantic features mined from online Wikipedia. This algorithm first identifies candidate keyphrases based on lexical methods, and then a semantic graph which connects candidate keyphrases with document topics is constructed. Afterwards, a link analysis algorithm is applied to assign semantic feature weight to the candidate keyphrases. Finally, several statistical and semantic features are assembled by a regression model to predict the quality of candidates. Encouraging results are achieved in experiments which show the effectiveness of method.