Finding topics in email using formal concept analysis and fuzzy membership functions

被引：0
作者：
Geng, Liqiang [1 ]
Korba, Larry [1 ]
Wang, Yunli [1 ]
Wang, Xin [2 ]
You, Yonghua [1 ]
机构：
[1] Natl Res Council Canada, Inst Informat Technol, Fredericton, NB, Canada
[2] Univ Calgary, Dept Geomat Engn, Calgary, AB, Canada
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE | 2008年 / 5032卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present a method to identify topics in email messages. ne formal concept analysis is adopted as a semantic analysis method to group emails containing the same keywords to concepts. The fuzzy membership functions are used to rank the concepts based on the features of the emails, such as the senders, recipients, time span, and frequency of emails in the concepts. The highly ranked concepts are then identified as email topics. Experimental results on the Enron email dataset illustrate the effectiveness of the method.
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收藏
页码：108 / +
页数：2
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