Difference between revisions of "An Empirical Research on Extracting Relations from Wikipedia Text"

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m ({{Infobox Publication |type=conference paper |title=An Empirical Research on Extracting Relations from Wikipedia Text |authors=Jin-Xia Huang, Pum-Mo Ryu, Key-Sun Choi |publishedin=IDEAL |keywords=Information extraction, feature-based, relatedness ...)
 
 
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|authors=Jin-Xia Huang, Pum-Mo Ryu, Key-Sun Choi
 
|authors=Jin-Xia Huang, Pum-Mo Ryu, Key-Sun Choi
 
|publishedin=IDEAL
 
|publishedin=IDEAL
|keywords=Information extraction, feature-based, relatedness information, relation classification
 
 
|date=2008
 
|date=2008
 
|pages=241-249
 
|pages=241-249
 +
|keywords=Information extraction, feature-based, relatedness information, relation classification
 +
|language=English
 +
|abstract=A feature based relation classification approach is presented, in which probabilistic and semantic relatedness features between patterns and relation types are employed with other linguistic information. The importance of each feature set is evaluated with Chi-square estimator, and the experiments show that, the relatedness features have big impact on the relation classification performance. A series experiments are also performed to evaluate the different machine learning approaches on relation classification, among which Bayesian outperformed other approaches including Support Vector Machine (SVM).
 
|isbn=978-3-540-88905-2
 
|isbn=978-3-540-88905-2
 
|doi=10.1007/978-3-540-88906-9_31
 
|doi=10.1007/978-3-540-88906-9_31
|language=English
 
|abstract=
 
 
}}
 
}}
 
 
{{talk}}
 
{{talk}}

Latest revision as of 09:22, October 10, 2012

An Empirical Research on Extracting Relations from Wikipedia Text is a 2008 conference paper written in English by Jin-Xia Huang, Pum-Mo Ryu, Key-Sun Choi and published in IDEAL.

[edit] Abstract

A feature based relation classification approach is presented, in which probabilistic and semantic relatedness features between patterns and relation types are employed with other linguistic information. The importance of each feature set is evaluated with Chi-square estimator, and the experiments show that, the relatedness features have big impact on the relation classification performance. A series experiments are also performed to evaluate the different machine learning approaches on relation classification, among which Bayesian outperformed other approaches including Support Vector Machine (SVM).

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