标题: | Concept Extraction and Clustering for Search Result Organization and Virtual Community Construction |
作者: | Chen, Shihn-Yuarn Chang, Chia-Ning Nien, Yi-Hsiang Ke, Hao-Ren 资讯管理与财务金融系 注:原资管所+财金所 Department of Information Management and Finance |
关键字: | information retrieval;concept extraction;document clustering;virtual community;social network analysis;bibliographic coupling |
公开日期: | 1-一月-2012 |
摘要: | This study proposes a concept extraction and clustering method, which improves Topic Keyword Clustering by using Log Likelihood Ratio for semantic correlation and Bisection K-Means for document clustering. Two value-added services are proposed to show how this approach can benefit information retrieval (IR) systems. The first service focuses on the organization and visual presentation of search results by clustering and bibliographic coupling. The second one aims at constructing virtual research communities and recommending significant papers to researchers. In addition to the two services, this study conducts quantitative and qualitative evaluations to show the feasibility of the proposed method; moreover, comparison with the previous approach is also performed. The experimental results show that the accuracy of the proposed method for search result organization reaches 80%, outperforming Topic Keyword Clustering. Both the precision and recall of virtual community construction are higher than 70%, and the accuracy of paper recommendation is almost 90%. |
URI: | http://hdl.handle.net/11536/16191 |
ISSN: | 1820-0214 |
期刊: | COMPUTER SCIENCE AND INFORMATION SYSTEMS |
Volume: | 9 |
Issue: | 1 |
结束页: | 323 |
显示于类别: | Articles |
文件中的档案:
If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.