A Novel Email Response Algorithm for Email Management Systems

Abstract

Email has been one of the most commonly used tool for communication in the recent years and email management has evolved as a major challenge due to prevailing situation of online email congestion. This study presents a novel algorithm for automatic email response methodology in an Email Management System to minimize email overload. The proposed model uses Bayes classifier to categorize emails into classes and generate suitable replies to these classes using information extraction and template filling. Our research aims to intelligently automate email response using Naïve Bayesian classification and formulate probabilistic dictionaries for accurate information extraction. This research will help in reducing email overload and unavoidable congestion by employing a novel email response architecture for an email management systems.

Improving Email Response in an Email Management System Using Natural Language Processing Based Probabilistic Methods

“…This research is presented as an extension of authors' previous study (Al-Alwani, 2014) on information extraction using Markov probabilistic methods model to train the email system facilitating prompt generation of an email reply. Previous research is improved upon by integrating Natural Language Processing to optimize information extraction by filtering overhead textual information.…”