| Download | - View accepted manuscript: Finding topics in email using formal concept analysis and fuzzy membership functions (PDF, 621 KB)
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| DOI | Resolve DOI: https://doi.org/10.1007/978-3-540-68825-9_11 |
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| Author | Search for: Geng, Liqiang1 ; Search for: Korba, Larry1 ; Search for: Wang, Yunli1 ; Search for: Wang, Xin; Search for: You, Yonghua1 |
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| Name affiliation | - National Research Council Canada. NRC Institute for Information Technology
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| Format | Text |
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| Type | Book chapter |
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| Proceedings title | Advances in Artificial Intelligence : 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings |
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| Series title | Lecture Notes In Computer Science; Volume 5032 |
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| Conference | The 21st Canadian Conference on Artificial Intelligence (CAI 2008), May 28-30, 2008, Windsor, Ontario, Canada |
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| ISSN | 0302-9743 |
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| Pages | 108–113; # of pages: 6 |
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| Abstract | In this paper, we present a method to identify topics in email messages. The 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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| Publication date | 2008 |
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| Publisher | Springer Berlin Heidelberg |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | 50327 |
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| NPARC number | 5765135 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction |
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Record identifier | c21739fa-1e09-4131-8286-d905874a1c19 | | Record created | 2009-03-29 |
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| Record modified | 2019-03-11 |
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