Title
Assessing e-mail intent and tasks in e-mail messages
Author
Sappelli, M.
Pasi, G.
Verberne, S.
de Boer, M.
Kraaij, W.
Publication year
2016
Abstract
In this paper, we analyze corporate e-mail messages as a medium to convey work tasks. Research indicates that categorization of e-mail could alleviate the common problem of information overload. Although e-mail clients provide possibilities of e-mail categorization, not many users spend effort on proper e-mail management. Since e-mail clients are often used for task management, we argue that intent- and task-based categorizations might be what is missing from current systems. We propose a taxonomy of tasks that are expressed through e-mail messages. With this taxonomy, we manually annotated two e-mail datasets (Enron and Avocado), and evaluated the validity of the dimensions in the taxonomy. Furthermore, we investigated the potential for automatic e-mail classification in a machine learning experiment. We found that approximately half of the corporate e-mail messages contain at least one task, mostly informational or procedural in nature. We show that automatic detection of the number of tasks in an e-mail message is possible with 71% accuracy. One important finding is that it is possible to use the e-mails from one company to train a classifier to classify e-mails from another company. Detecting how many tasks a message contains, whether a reply is expected, or what the spatial and time sensitivity of such a task is, can help in providing a more detailed priority estimation of the message for the recipient. Such a priority-based categorization can support knowledge workers in their battle against e-mail overload. © 2016 Elsevier Inc. All rights reserved.
Subject
ICT
DSC - Data Science
TS - Technical Sciences
E-mail annotation scheme
E-mail intent
Human annotation
Task-based e-mail categorization
Artificial intelligence
Learning systems
Taxonomies
Annotation scheme
Automatic Detection
E-mail management
Email categorization
Email classification
Human annotations
Information overloads
Support knowledge
Electronic mail
To reference this document use:
http://resolver.tudelft.nl/uuid:e6dde4e9-6125-4237-b756-b56369c59d25
DOI
https://doi.org/10.1016/j.ins.2016.03.002
TNO identifier
535482
Publisher
Elsevier Inc.
ISSN
0020-0255
Source
Information Sciences, 358-359, 1-17
Document type
article