Measuring semantic relatedness using wikipedia signed network
|Measuring semantic relatedness using wikipedia signed network|
|Author(s)||Yang W.-T., Kao H.-Y.|
|Published in||Journal of Information Science and Engineering|
|Keyword(s)||HITS, Semantic relatedness, Signed network, Wikipedia (Extra: 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))|
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Identifying 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.
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