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. Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques. Today’s email clients were designed for yesterday’s email. Originally, email was merely a communication medium. Today, people engage in a variety of complex behaviours using email, such as project management, collaboration, meeting scheduling, to-do tracking, etc. Our goal is to develop automated techniques to help people manage complex activities or tasks in email. The central challenge is that most activities are distributed over multiple messages, yet email clients allow users to manipulate just isolated messages. We describe machine learning approaches to identifying tasks and relations between individual messages in a task (i.e., finding cause-response links between emails) and for semantic message analysis (i.e., extracting metadata about how messages within a task relate to the task progress). Our key innovation compared to related work is that we exploit the relational structure of these two problems. Instead of attacking them separately, in our synergistic iterative approach, relations identification is used to assist semantic analysis, and vice versa. Our experiments with real-world email corpora demonstrate an improvement compared to nonrelational benchmarks. We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify and classify names of people, locations and organisations in text. This dependence on expensive annotation is the knowledge bottleneck our work overcomes. We first classify each Wikipedia article into named entity (ne) types, training and evaluating on 7200 manually-labelled Wikipedia articles across nine languages. Our cross-lingual approach achieves up to 95 accuracy. We transform the links between articles into ne annotations by projecting the target [email protected]?s classifications onto the anchor text. This approach yields reasonable annotations, but does not immediately compete with existing gold-standard data. By inferring additional links and heuristically tweaking the Wikipedia corpora, we better align our automatic annotations to gold standards. We annotate millions of words in nine languages, evaluating English, German, Spanish, Dutch and Russian Wikipedia-trained models against conll shared task data and other gold-standard corpora. Our approach outperforms other approaches to automatic ne annotation (Richman and Schone, 2008 [61], , 2008 [46]) competes with gold-standard training when tested on an evaluation corpus from a different source; and performs 10 better than newswire-trained models on manually-annotated Wikipedia text.