学习识别描述电子邮件活动的正则表达式
Learning to Identify Regular Expressions that Describe Email Campaigns
摘要：
本文讨论了从一组给定的字符串中推断正则表达式的问题，这些字符串尽可能接近人类专家为识别语言而编写的正则表达式。这是由我们的目标，自动化的任务，邮政管理员的电子邮件服务，谁使用正则表达式来描述和黑名单的电子邮件垃圾邮件活动。训练数据包含成批的消息和相应的正则表达式，专家级的邮政管理员有信心将其列入黑名单。我们将此任务建模为具有结构化输出空间和适当损失函数的学习问题，推导出解码器和由此产生的优化问题，并报告了使用电子邮件服务进行的案例研究。
英文摘要：
This paper addresses the problem of inferring a regular expression from a given set of strings that resembles, as closely as possible, the regular expression that a human expert would have written to identify the language. This is motivated by our goal of automating the task of postmasters of an email service who use regular expressions to describe and blacklist email spam campaigns. Training data contains batches of messages and corresponding regular expressions that an expert postmaster feels confident to blacklist. We model this task as a learning problem with structured output spaces and an appropriate loss function, derive a decoder and the resulting optimization problem, and a report on a case study conducted with an email service.
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。
