چکیده (انگلیسی):

Email based communication over the course of globalization in
recent years has transformed into an all-encompassing form of interaction
and requires automatic processes to control email correspondence in an
environment of increasing email database. Relevance characteristics
defining class of email in general includes the topic of thee mail and the
sender of the email along with the body of email. Intelligent reply
algorithms can be employed in which machine learning methods can
accommodate email content using probabilistic methods to classify
context and nature of email. This helps in correct selection of template for
email reply. Still redundant information can cause errors in classifying an
email. Natural Language Processing (NLP) possess potential in
optimizing text classification due to its direct relation with language
structure. An enhancement is presented in this research to address email
management issues by incorporating optimized information extraction for
email classification along with generating relevant dictionaries as emails
vary in categories and increases in volume. The open hypothesis of this
research is that the underlying concept to fan email is communicating a
message in form of text. It is observed that NLP techniques improve
performance of Intelligent Email Reply algorithm enhancing its ability to
classify and generate email responses with minimal errors using
probabilistic methods. Improved algorithm is functionally automated with
machine learning techniques to assist email users who find it difficult to
manage bulk variety of emails.