Measuring semantic relatedness using wikipedia signed network

Wen Teng Yang, Hung-Yu Kao

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)615-630
頁數16
期刊Journal of Information Science and Engineering
29
發行號4
出版狀態Published - 2013 7月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人機介面
  • 硬體和架構
  • 計算機理論與數學
  • 圖書館與資訊科學

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