Detecting emails containing requests for action

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.