Detecting emails containing requests for action

Andrew Lampert*, Robert Dale, Cecile Paris

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

33 Citations (Scopus)

Abstract

Automatically finding email messages that contain requests for action can provide valuable assistance to users who otherwise struggle to give appropriate attention to the actionable tasks in their inbox. As a speech act classification task, however, automatically recognising requests in free text is particularly challenging. The problem is compounded by the fact that typical emails contain extraneous material that makes it difficult to isolate the content that is directed to the recipient of the email message. In this paper, we report on an email classification system which identifies messages containing requests; we then show how, by segmenting the content of email messages into different functional zones and then considering only content in a small number of message zones when detecting requests, we can improve the accuracy of message-level automated request classification to 83.76%, a relative increase of 15.9%. This represents an error reduction of 41% compared with the same request classifier deployed without email zoning.

Original languageEnglish
Title of host publicationNAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics (ACL)
Pages984-992
Number of pages9
ISBN (Print)1932432655, 9781932432657
Publication statusPublished - 2010
Event2010 Human Language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010 - Los Angeles, CA, United States
Duration: 2 Jun 20104 Jun 2010

Other

Other2010 Human Language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010
CountryUnited States
CityLos Angeles, CA
Period2/06/104/06/10

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