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Measuring semantic relatedness using wikipedia signed network
Abstract Identifying the semantic relatedness of twIdentifying the semantic relatedness of two words is an important task for the information retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a valuable resource for research work. Wikipedia articles, written by a live collaboration of user editors, contain a high volume of reference links, URL identification for concepts and a complete revision history. Moreover, each Wikipedia article represents an individual concept that simultaneously contains other concepts that are hyperlinks of other articles embedded in its content. Through this, we believe that the semantic relatedness between two words can be found through the semantic relatedness between two Wikipedia articles. Therefore, we propose an Editor-Contribution-based Rank (ECR) algorithm for ranking the concepts in the article's content through all revisions and take the ranked concepts as a vector representing the article. We classify four types of relationship in which the behavior of addition and deletion maps appropriate and inappropriate concepts. ECR also extend the concept semantics by the editor-concept network. ECR ranks those concepts depending on the mutual signed-reinforcement relationship between the concepts and the editors. The results reveal that our method leads to prominent performance improvement and increases the correlation coefficient by a factor ranging from 4% to 23% over previous methods that calculate the relatedness between two articles.late the relatedness between two articles.
Abstractsub Identifying the semantic relatedness of twIdentifying the semantic relatedness of two words is an important task for the information retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a valuable resource for research work. Wikipedia articles, written by a live collaboration of user editors, contain a high volume of reference links, URL identification for concepts and a complete revision history. Moreover, each Wikipedia article represents an individual concept that simultaneously contains other concepts that are hyperlinks of other articles embedded in its content. Through this, we believe that the semantic relatedness between two words can be found through the semantic relatedness between two Wikipedia articles. Therefore, we propose an Editor-Contribution-based Rank (ECR) algorithm for ranking the concepts in the article's content through all revisions and take the ranked concepts as a vector representing the article. We classify four types of relationship in which the behavior of addition and deletion maps appropriate and inappropriate concepts. ECR also extend the concept semantics by the editor-concept network. ECR ranks those concepts depending on the mutual signed-reinforcement relationship between the concepts and the editors. The results reveal that our method leads to prominent performance improvement and increases the correlation coefficient by a factor ranging from 4% to 23% over previous methods that calculate the relatedness between two articles.late the relatedness between two articles.
Bibtextype article  +
Has author Yang W.-T. + , Kao H.-Y. +
Has extra keyword Correlation coefficient + , HITS + , Individual concepts + , NAtural language processing + , Performance improvements + , Semantic relatedness + , Signed networks + , Wikipedia + , Data mining + , Hypertext systems + , Natural language processing systems + , Semantics + , Social networking (online) +
Has keyword HITS + , Semantic relatedness + , Signed network + , Wikipedia +
Issn 10162364  +
Issue 4  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 615–630  +
Published in Journal of Information Science and Engineering +
Title Measuring semantic relatedness using wikipedia signed network +
Type journal article  +
Volume 29  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 8 November 2014 00:29:56  +
Categories Publications without license parameter  + , Publications without DOI parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 00:29:56  +
DateThis property is a special property in this wiki. 2013  +
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