Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business processes of companies and institutions. Towards this aim, a few researchers investigated the problem of extracting process oriented information from email logs in order to take benefit of the many available process mining techniques and tools. In this paper we go further in this direction, by proposing a new method for mining process models from email logs that leverage unsupervised machine learning techniques with little human involvement. Moreover, our method allows to semi-automatically label emails with activity names, that can be used for activity recognition in new incoming emails. A use case demonstrates the usefulness of the proposed solution using a modest in size, yet real-world, dataset containing emails that belong to two different process models
SmartMail in Corston- @cite_0 identifies action items(tasks) in email messages. It produces summary for each email which can be added by the user to his her "to-do" list. In their approach, they need human annotators to provide tags to the training data set. Despite the extensive studies of speech act recognition in many areas, developing speech act recognition for emails is very challenging. A major challenge is that emails usually have no labeled data for training statistical speech acts recognizers.
@cite_0
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We describe SmartMail, a prototype system for automatically identifying action items (tasks) in email messages. SmartMail presents the user with a task-focused summary of a message. The summary consists of a list of action items extracted from the message. The user can add these action items to their “to do” list.