Intelligent deep learning based bidirectional long short term memory model for automated reply of e-mail client prototype

Abstract

E-mail is considered the commonly used and efficient way of communication over the globe. In the corporate sectors, the number of E-mails received every day is considerably high and the timely response to every E-mail is essential. Several researchers believe that natural language processing (NLP) techniques by the use of deep learning (DL) architectures have played a considerable part to reduce manual work for repeated E-mail responses and intended to develop E-mail systems with intelligent response function. In this view, this paper designs an intelligent DL enabled optimal bidirectional long short term memory (Bi-LSTM) technique for an automated E-mail reply (OBiLSTM-AER) of E-mail Client Prototype. The goal of the proposed model is to provide an automated E-mail reply solution for persons as well as corporates which receive massive identical E-mails daily. The presented model employs Glove and OBiLSTM model for feature extraction of receiving and response E-mails respectively. Finally, softmax classifier is applied to allocate the class labels. For improving the performance of the BiLSTM model, the hyperparameter tuning process takes place using an oppositional glowworm swarm optimization (OGSO) algorithm. An extensive set of simulations were performed to highlight the betterment of the proposed method and the results are examined interms of distinct measures.