The nature of statistical learning theory
The nature of statistical learning theory
Email archive overviews using subject indexes
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Exploring discussion lists: steps and directions
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning part-of-speech guessing rules from lexicon: extension to non-concatenative operations
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Combining linguistic and machine learning techniques for email summarization
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Conversation Map: An Interface for Very Large-Scale Conversations
Journal of Management Information Systems
Discovery and regeneration of hidden emails
Proceedings of the 2005 ACM symposium on Applied computing
Scalable discovery of hidden emails from large folders
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Automatically selecting answer templates to respond to customer emails
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automate back office activity monitoring to drive operational excellence
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
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The explosive growth of the Internet has made email an integral part of business communication. Therefore, business customer service centers, or contact centers, are processing larger amounts of email interactions with customers. In this paper we discuss a preliminary email routing and classification system that filters and classifies incoming email messages upon their content. A module first attempts to identify and filter those email messages that do not require immediate (if any) responses. We call such email messages single messages. The emails that do require immediate responses are called root messages. A second module classifies messages in categories that characterize the type of interaction between the contact center operators and the customers. Emails that are involved in such interactions form a thread and can be classified broadly into one of three categories: root, inner, and leaf. Root messages are those that start a thread while a leaf message is the final email sent in an interaction. All other emails in the interaction are considered to be inner messages.