DocumentCode :
1964377
Title :
Improving the Automatic Email Responding System for computer manufacturers via machine learning
Author :
Weiwen Yang ; Linchi Kwok
Author_Institution :
Sch. of Eng. & Appl. Sci., Columbia Univ., New York, NY, USA
Volume :
3
fYear :
2012
fDate :
20-21 Oct. 2012
Firstpage :
487
Lastpage :
491
Abstract :
Computer manufacturers consist of multiple departments, and each department has its own duties and functions. As email is often used as the primary communication tool for computer industries, the customer service departments of the computer manufacturers receive a large number of emails daily. The emails need to be forwarded to the corresponding personnel and processed. Excessive resources and time are spent in manually reading and answering customers´ emails. An automatic reply tool is crucial in reducing company resources for manually processing customers´ emails. This paper discusses how to improve processing emails automatically for computer manufacturers. Specifically, emails from senders are classified into multiple categories by machine learning algorithms. The reply for each email category is predefined. The Automatic Email Responding System (AERS) replies to an email based on the predefined, corresponding category. This experiment shows that the automatic tool can process and answer emails efficiently.
Keywords :
DP industry; customer services; electronic mail; learning (artificial intelligence); AERS; automatic e-mail processing improvement; automatic e-mail responding system; automatic reply tool; company resource reduction; computer industries; computer manufacturers; customer e-mail answering; customer e-mail reading; customer service departments; machine learning algorithms; primary communication tool; Electronic mail; Manuals; Niobium; Automatic Categorization; Email System; Machine Learning; Naive Bayes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Management, Innovation Management and Industrial Engineering (ICIII), 2012 International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4673-1932-4
Type :
conf
DOI :
10.1109/ICIII.2012.6340024
Filename :
6340024
Link To Document :
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