安然语料库：电子邮件尸体被埋葬的地方？
The Enron Corpus: Where the Email Bodies are Buried?
摘要：
为了探索最大的公共领域电子邮件数据库的欺诈指标，我们应用机器学习，并完成四个调查任务。首先，我们使用财务记录和电子邮件识别感兴趣的人（POI），并报告95.7%的峰值准确率。第二，我们发现任何公开的个人身份信息（PII），并发现50，000个以前未报告的实例。第三，我们自动标记合法响应的电子邮件，由加州停电诉讼中的人类专家评分，并找到99%的准确率。最后，我们追踪了超过10，000人在企业危机爆发之前、期间和之后三年的主要话题和情绪。在可能的情况下，我们比较了51种算法的准确性和执行时间，并报告了可以扩展到大型数据集的人类可解释的业务规则。
英文摘要：
To probe the largest public-domain email database for indicators of fraud, we apply machine learning and accomplish four investigative tasks. First, we identify persons of interest (POI), using financial records and email, and report a peak accuracy of 95.7%. Secondly, we find any publicly exposed personally identifiable information (PII) and discover 50,000 previously unreported instances. Thirdly, we automatically flag legally responsive emails as scored by human experts in the California electricity blackout lawsuit, and find a peak 99% accuracy. Finally, we track three years of primary topics and sentiment across over 10,000 unique people before, during and after the onset of the corporate crisis. Where possible, we compare accuracy against execution times for 51 algorithms and report human-interpretable business rules that can scale to vast datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
