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Applying machine learning techniques for e-mail management: solution with intelligent e-mail reply prediction

机译:将机器学习技术应用于电子邮件管理:具有智能电子邮件回复预测的解决方案

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摘要

In today’s world, much of our communication is done via e-mail. Many companies and internet users now view e-mail as one of their most critical personal and business applications and would experience serious consequences if their e-mail messages could not be available or experience high volume of messages which lead to congestions, overloads and limited storage space coupled with un-organized e-mail messages. A few years ago, the means of communication are via letter by post, telegraph, fax, couriers to mention a few but now the focus has changed to a faster means of obtaining quick responses and faster ways of communication, e-mails. We propose a new framework to help organised and prioritized e-mail better; e-mail reply prediction. The goal is to provide concise, highly structured and prioritized e-mails, thus saving the user from browsing through each email one by one and help to save time.
机译:在当今世界,我们的大部分沟通都是通过电子邮件进行的。现在,许多公司和互联网用户将电子邮件视为他们最重要的个人和业务应用程序之一,如果无法获得电子邮件消息或遇到大量消息导致拥塞,超载和存储受限,将会遭受严重后果空间,加上杂乱无章的电子邮件。几年前,通讯方式是通过邮寄,电报,传真,快递等方式,但现在的焦点已经转向更快地获得快速响应和更快的通讯方式,电子邮件的方式。我们提出了一个新框架,以帮助更好地组织和优先处理电子邮件。电子邮件回复预测。目的是提供简洁,高度结构化和优先级高的电子邮件,从而使用户免于逐一浏览每封电子邮件,并节省了时间。

著录项

  • 作者

    Ayodele Taiwo; Zhou Shikun;

  • 作者单位
  • 年度 2009
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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