NDLI: Automatically classifying emails into activities

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Automatically classifying emails into activities

Content Provider	ACM Digital Library
Author	Dredze, Mark Kushmerick, Nicholas Lau, Tessa
Abstract	Email-based activity management systems promise to give users better tools for managing increasing volumes of email, by organizing email according to a user's activities. Current activity management systems do not automatically classify incoming messages by the activity to which they belong, instead relying on simple heuristics (such as message threads), or asking the user to manually classify incoming messages as belonging to an activity. This paper presents several algorithms for automatically recognizing emails as part of an ongoing activity. Our baseline methods are the use of message reply-to threads to determine activity membership and a naïve Bayes classifier. Our SimSubset and SimOverlap algorithms compare the people involved in an activity against the recipients of each incoming message. Our SimContent algorithm uses IRR (a variant of latent semantic indexing) to classify emails into activities using similarity based on message contents. An empirical evaluation shows that each of these methods provide a significant improvement to the baseline methods. In addition, we show that a combined approach that votes the predictions of the individual methods performs better than each individual method alone.
Starting Page	70
Ending Page	77
Page Count	8
File Format	PDF
ISBN	1595932879
DOI	10.1145/1111449.1111471
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-01-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Activity management Email Machine learning Text classification
Content Type	Text
Resource Type	Article

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