A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Active learning for logistic regression
Active learning for logistic regression
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance thresholding in practical text classification
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A stopping criterion for active learning
Computer Speech and Language
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
An intrinsic stopping criterion for committee-based active learning
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Tag confidence measure for semi-automatically updating named entity recognition
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Confidence-based stopping criteria for active learning for data annotation
ACM Transactions on Speech and Language Processing (TSLP)
Using smaller constituents rather than sentences in active learning for Japanese dependency parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using variance as a stopping criterion for active learning of frame assignment
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Bringing active learning to life
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Contextual recommendation based on text mining
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Inactive learning?: difficulties employing active learning in practice
ACM SIGKDD Explorations Newsletter
Evaluating the impact of coder errors on active learning
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
| Hi-index | 0.00 |
Active learning is a proven method for reducing the cost of creating the training sets that are necessary for statistical NLP. However, there has been little work on stopping criteria for active learning. An operational stopping criterion is necessary to be able to use active learning in NLP applications. We investigate three different stopping criteria for active learning of named entity recognition (NER) and show that one of them, gradient-based stopping, (i) reliably stops active learning, (ii) achieves nearoptimal NER performance, (iii) and needs only about 20% as much training data as exhaustive labeling.