Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised learning of attribute-value pairs from product descriptions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An extension of the aspect PLSA model to active and semi-supervised learning for text classification
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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Research in multi-view active learning has typically focused on algorithms for selecting the next example to label. This is often at the cost of lengthy wait-times for the user between each query iteration. We deal with a real-world information extraction task, extracting attribute-value pairs from product descriptions, where the learning system needs to be interactive and the user's time needs to be used efficiently. The first step uses coEM with naive Bayes as the semi-supervised algorithm. This paper focuses on the second step which is an interactive active learning phase. We present an approximation to coEM with naive Bayes that can incorporate user feedback almost instantly and can use any sample-selection strategy for active learning. Our experimental results show high levels of accuracy while being orders of magnitude faster than using the standard coEM with naive Bayes, making our IE system practical by optimizing user time.