Enron Dataset Research: E-mail Relevance Classification

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dc.contributor.authorVanBuren, Victoria
dc.contributor.authorVillarreal, David
dc.contributor.authorMcMillen, Thomas A.
dc.contributor.authorMinnicks, Andrew L.
dc.date.accessioned2009-10-14T10:03:29Z
dc.date.available2012-02-24T10:03:31Z
dc.date.issued2009-09-25
dc.description.abstractThis paper discusses a probabilistic approach to address the problem of searching through large amount of data to find case-relevant documents. Using a valuable collection of data, e-mail communications from Enron, an actual corporation, we train a Bayes-based text classifier algorithm to identify e-mails known to be case-relevant and those known to be case-irrelevant.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent16 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationVanBuren, V., Villarreal, D., McMillen, T. A., & Minnick, A. L. (2009). Enron dataset research: E-mail relevance classification (Report No. TXSTATE-CS-TR-2009-12). Texas State University-San Marcos, Department of Computer Science.
dc.identifier.urihttps://hdl.handle.net/10877/2583
dc.language.isoen
dc.subjectenron dataset
dc.subjecte-mail Relevance
dc.subjecte-mail classification
dc.subjectBayes classifier
dc.subjectelectronic discovery
dc.subjectforensics
dc.subjectComputer Science
dc.titleEnron Dataset Research: E-mail Relevance Classification
dc.typeTechnical Report

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