Information Retrieval
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Interactive annotation learning with indirect feature voting
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Generalizing dependency features for opinion mining
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Active learning by labeling features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Sentiment classification using automatically extracted subgraph features
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Feature feedback is an alternative to instance labeling when seeking supervision from human experts. Combination of instance and feature feedback has been shown to reduce the total annotation cost for supervised learning. However, learning problems may not benefit equally from feature feedback. It is well understood that the benefit from feature feedback reduces as the amount of training data increases. We show that other characteristics such as domain, instance granularity, feature space, instance selection strategy and proportion of relevant text, have a significant effect on benefit from feature feedback. We estimate the maximum benefit feature feedback may provide; our estimate does not depend on how the feedback is solicited and incorporated into the model. We extend the complexity measures proposed in the literature and propose some new ones to categorize learning problems, and find that they are strong indicators of the benefit from feature feedback.