Email reply prediction: unsupervised leaning approach

Taiwo Ayodele; Shikun Zhou

doi:10.1109/ICDIM.2008.4746844

Email reply prediction: unsupervised leaning approach

Taiwo Ayodele^*, Shikun Zhou

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 3rd International Conference on Digital Information Management, ICDIM 2008
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	844-849
Number of pages	6
ISBN (Print)	9781424429172
DOIs	https://doi.org/10.1109/ICDIM.2008.4746844
Publication status	Published - 9 Jan 2009
Event	3rd International Conference on Digital Information Management, ICDIM 2008 - London, United Kingdom Duration: 13 Nov 2008 → 16 Nov 2008

Conference

Conference	3rd International Conference on Digital Information Management, ICDIM 2008
Country/Territory	United Kingdom
City	London
Period	13/11/08 → 16/11/08

Keywords

Email reply prediction
Emails
Interrogative words
Questions
Require reply

Access to Document

10.1109/ICDIM.2008.4746844

Cite this

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title = "Email reply prediction: unsupervised leaning approach",

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.",

keywords = "Email reply prediction, Emails, Interrogative words, Questions, Require reply",

author = "Taiwo Ayodele and Shikun Zhou",

year = "2009",

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doi = "10.1109/ICDIM.2008.4746844",

language = "English",

isbn = "9781424429172",

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booktitle = "Proceedings of the 3rd International Conference on Digital Information Management, ICDIM 2008",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "3rd International Conference on Digital Information Management, ICDIM 2008 ; Conference date: 13-11-2008 Through 16-11-2008",

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Ayodele, T & Zhou, S 2009, Email reply prediction: unsupervised leaning approach. in Proceedings of the 3rd International Conference on Digital Information Management, ICDIM 2008. Institute of Electrical and Electronics Engineers Inc., pp. 844-849, 3rd International Conference on Digital Information Management, ICDIM 2008, London, United Kingdom, 13/11/08. https://doi.org/10.1109/ICDIM.2008.4746844

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AU - Ayodele, Taiwo

AU - Zhou, Shikun

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N2 - 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.

AB - 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.

KW - Email reply prediction

KW - Emails

KW - Interrogative words

KW - Questions

KW - Require reply

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DO - 10.1109/ICDIM.2008.4746844

M3 - Conference contribution

AN - SCOPUS:62949175389

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BT - Proceedings of the 3rd International Conference on Digital Information Management, ICDIM 2008

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 November 2008 through 16 November 2008

ER -

Email reply prediction: unsupervised leaning approach

Abstract

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Keywords

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