Relational Learning for Email Task Management

Abstract

Today’s email clients were designed for yesterday’s email. Originally, email was merely a communication medium. Today, email has become a “habitat” [Ducheneaut and Bellotti, 2001]—an environment where users engage in a variety of complex activities. Our goal is to develop automated techniques to help people manage complex activities or tasks in email. In many cases, such activities manifest the user’s participation in various structured processes or workflows. The central challenge is that most processes are distributed over multiple emails, yet email clients are designed mainly to manipulate individual messages. A task-oriented email client would allow the user to manage activities rather than separate messages. For instance, the user would be able to quickly inquire about the current status of unfinished e-commerce transactions or check the outcome of recent project meetings. Some process steps could be automated, such as automatically sending reminders for earlier user’s requests. Similarly, the email client could remind the user when her/his input is required in some activity. Previous work in this area has mainly focused on two distinct problems: finding related messages and semantic message analysis. The goal of finding related messages is to group emails according to tasks and possibly establish conversational links between emails in a task (e.g. extract a task from email given a seed message [Dredze, 2005]). Note that tasks need not correspond to folders (folders can be orthogonal to tasks); and conversations need not correspond to syntactic threads (users can use the “Reply” button or the same subject to start a semantically new conversation). Semantic message analysis involves generating metadata for individual messages in a task that provides a link between the messages and the changes in the status of the underlying process, or the actions of the user in the underlying workflow. For example, [Cohen et al., 2004] proposed machine learning methods to classify emails according to the intent of the sender, expressed in an ontology of “email speech acts”. Our key innovation compared to related work is that we exploit the relational structure of these two tasks. The idea is that related messages in a task provide a valuable context that can be used for semantic message analysis. Similarly, the activity-related metadata in separate messages can pro-