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.
Highlights
Email is one of the most reliable means of online correspondence and has become an essential communication tool for most organizations and individuals
This study presents a novel algorithm for automatic email response methodology in an Email Management System to minimize email overload
Our research aims to intelligently automate email response using Naïve Bayesian classification and formulate probabilistic dictionaries for accurate information extraction
Summary
Email is one of the most reliable means of online correspondence and has become an essential communication tool for most organizations and individuals. To assist users in automated email replies, with correct classification and timely prioritization, we present a novel algorithm for generating automated priority reply to emails after appropriate classification. Naïve Bayes is a popular method, a frequently used machine learning model for several years Its simplicity allow it to be used manner in many applications and good classification results are obtained using this learning approach despite its dependence on an unrealistic independence assumptions. Abdulkareem Al-Alwani / Journal of Computer Science 10 (4): 689-696, 2014 probability model is used to train the email system on previously observed patterns to facilitate appropriate template selection for email reply Overall, this in this research we have proposed a novel algorithm to facilitate email management systems by generating automated intelligent replies to selected class of emails. A detailed description of the proposed algorithm is presented in section 3 followed by conclusion with closing remarks on future course of work
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