Email reply prediction: unsupervised leaning approach

Taiwo Ayodele*, Shikun Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize their email messages, we propose a new framework to help organised and prioritized email better; email reply prediction. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Digital Information Management, ICDIM 2008
PublisherIEEE
Pages844-849
Number of pages6
ISBN (Print)9781424429172
DOIs
Publication statusPublished - 9 Jan 2009
Event3rd International Conference on Digital Information Management, ICDIM 2008 - London, United Kingdom
Duration: 13 Nov 200816 Nov 2008

Conference

Conference3rd International Conference on Digital Information Management, ICDIM 2008
Country/TerritoryUnited Kingdom
CityLondon
Period13/11/0816/11/08

Keywords

  • Email reply prediction
  • Emails
  • Interrogative words
  • Questions
  • Require reply

Fingerprint

Dive into the research topics of 'Email reply prediction: unsupervised leaning approach'. Together they form a unique fingerprint.

Cite this