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- W156710820 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2006 SIAM International Conference on Data Mining (SDM)Name Reference Resolution in Organizational Email ArchivesChristopher P. Diehl, Lise Getoor, and Galileo NamataChristopher P. DiehlJohns Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MDComputer Science Department/UMIACS, University of Maryland, College Park, MDComputer Science Department/UMIACS, University of Maryland, College Park, MDSearch for more papers by this author, Lise GetoorJohns Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MDComputer Science Department/UMIACS, University of Maryland, College Park, MDComputer Science Department/UMIACS, University of Maryland, College Park, MDSearch for more papers by this author, and Galileo NamataJohns Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MDComputer Science Department/UMIACS, University of Maryland, College Park, MDComputer Science Department/UMIACS, University of Maryland, College Park, MDSearch for more papers by this authorpp.70 - 81Chapter DOI:https://doi.org/10.1137/1.9781611972764.7PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Online communications provide a rich resource for understanding social networks. Information about the actors, and their dynamic roles and relationships, can be inferred from both the communication content and traffic structure. A key component in the analysis of online communications such as email is the resolution of name references within the body of the message. Name reference resolution relies on the context of the message; both the content of the message and the sender and recipients' relationships can help to resolve a reference. Here we investigate a variety of approaches which make use of the email traffic network to disambiguate email name references. The email traffic network serves as a proxy for inferring relationships. These relationships in turn help us infer likely candidates for the name references. Our initial findings suggest that simple temporal models can help us effectively resolve name references. For the class of models proposed, performance is maximized by exploiting long-term traffic statistics to rank candidates. Previous chapter Next chapter RelatedDetails Published:2006ISBN:978-0-89871-611-5eISBN:978-1-61197-276-4 https://doi.org/10.1137/1.9781611972764Book Series Name:ProceedingsBook Code:PR124Book Pages:xii + 646" @default.
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- W156710820 title "Name Reference Resolution in Organizational Email Archives" @default.
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