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
A maximum entropy approach to natural language processing
Computational Linguistics
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Minimizing manual annotation cost in supervised training from corpora
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
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
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
Performance thresholding in practical text classification
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Stopping criteria for active learning of named entity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Multi-criteria-based strategy to stop active learning for data annotation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Active learning for part-of-speech tagging: accelerating corpus annotation
LAW '07 Proceedings of the Linguistic Annotation Workshop
A two-stage method for active learning of statistical grammars
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reverse active learning for optimising information extraction training production
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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As supervised machine learning methods are increasingly used in language technology, the need for high-quality annotated language data becomes imminent. Active learning (AL) is a means to alleviate the burden of annotation. This paper addresses the problem of knowing when to stop the AL process without having the human annotator make an explicit decision on the matter. We propose and evaluate an intrinsic criterion for committee-based AL of named entity recognizers.