Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Concept features in Re:Agent, an intelligent Email agent
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Making large-scale support vector machine learning practical
Advances in kernel methods
MailCat: an intelligent assistant for organizing e-mail
Proceedings of the third annual conference on Autonomous Agents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Modern Information Retrieval
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
IEMS - The Intelligent Email Sorter
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Automated email answering by text pattern matching
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Maintaining rule friendly for email management
MIV'05 Proceedings of the 5th WSEAS international conference on Multimedia, internet & video technologies
DCPE co-training for classification
Neurocomputing
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Many individuals, organizations, and companies have to answer large amounts of emails. Often, many of these emails contain variations of relatively few frequently asked questions. We address the problem of predicting which of several frequently used answers a user will choose to respond to an email. We map the problem to a semi-supervised text classification problem. In a case study with emails that have been sent to a corporate customer service department, we investigate the ability of the naive Bayesian and support vector classifier to identify the appropriate answers to emails. We study how effectively the transductive Support Vector Machine and the co-training algorithm utilize unlabeled data and investigate why co-training is only beneficial when very few labeled data are available. In addition, we describe a practical assistance system.