Context‐based email classification model

Abstract

Context‐based email classification requires understanding of semantic and structural attributes of email. Most of the research has focused on generating semantic properties through structural components of email. By viewing emails as events (as a major subset of class of email), a rich contextual test‐bed representation for understanding of the semantic attributes of emails has been devised. The event‐ based emails have traditionally been studied based on simple structural properties. In this paper, we present a novel approach by first representing such class of emails as graphs, followed by heuristically applying graph mining and matching algorithm to pick templates representing contextual and semantic attributes that help classify emails. The classification templates used three key event classes: social, personal and professional. Results show that our graph mining and matching supported template‐based approach performs consistently well over event email data set with high accuracy.

Information Processing in EventWeb through Detection and Analysis of Connections between Events

“…This work accomplishes the development of one of the components of our context-based event detection model. Therefore, we have sampled the event email data set constructed for our two previously developed components [21,2]. Our motivation for sampling the university data set is:  The email data set contains an extensive variety of event related communications.…”

“…The selection of our dataset towards the university email corpus itself posed some interesting problems such as the categorization of the variety of event types, spanning from one-one lunch invitations to faculty meetings, office meetings, discussions with the supervisor, wedding invitations, educational and social seminars, conferences, picnic, and sport events etc. that were covered in our dataset [2].…”

“…The data was extracted using an Event Information Extraction System (EIES) that uses part of speech (POS) based Finite State Machines (FSM) for extraction of event information (title, location, temporal information and participants) from emails [21]. We then used our event email classifier to label the extracted events with an event category (Social, Professional, or Personal) [2]. Finally, the collected contextual event information is stored in CSV format, and fed as input to EConnDetect.…”

“…For this, we initially extracted these events from email inboxes of several students and faculty members (with their consent). Using our previous technologies, we then extracted event information attributes by using finite state machines and then used an event classifier to tag the events with proper categories [1,2]. We then provided these event attributes as input to EConnDetect, and calculated precision and recall for the identified connections as our evaluation metrics.…”

An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

“…In the literature, machine learning has been widely studied in email classification. 8,9 Different supervised learning algorithms are examined such as Decision Tree, 10 support vector machine (SVM), 11 Naive Bayes, 12 k-nearest neighbor (KNN), 13 and deep leaning. 14 However, it is well known that the performance of machine learning algorithms would be unstable and fluctuant according to concrete data sets.…”