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An empirical research on extracting relations from Wikipedia text
Abstract A feature based relation classification apA 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).es including Support Vector Machine (SVM).
Abstractsub A feature based relation classification apA 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).es including Support Vector Machine (SVM).
Bibtextype inproceedings  +
Doi 10.1007/978-3-540-88906-9-31  +
Has author Huang J.-X. + , Ryu P.-M. + , Choi K.-S. +
Has extra keyword Information analysis + , Information theory + , Learning systems + , Support vector machines + , Text processing + , Bayesian + , Classification approaches + , Classification performances + , Empirical researches + , Feature sets + , Feature-based + , Information extraction + , Linguistic informations + , Machine learnings + , Relatedness information + , Relation classification + , Semantic relatednesses + , Wikipedia + , Feature extraction +
Has keyword Feature-based + , Information extraction + , Relatedness information + , Relation classification +
Isbn 3540889051; 9783540889052  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 241–249  +
Published in Lecture Notes in Computer Science +
Title An empirical research on extracting relations from Wikipedia text +
Type conference paper  +
Volume 5326 LNCS  +
Year 2008 +
Creation dateThis property is a special property in this wiki. 6 November 2014 19:10:09  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 19:10:09  +
DateThis property is a special property in this wiki. 2008  +
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