COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth 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
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Minimizing manual annotation cost in supervised training from corpora
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Rule writing or annotation: cost-efficient resource usage for base noun phrase chunking
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Interactive information extraction with constrained conditional random fields
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Bucking the trend: large-scale cost-focused active learning for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A cognitive cost model of annotations based on eye-tracking data
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Active semi-supervised learning for improving word alignment
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Active learning for sequence labelling with probability re-estimation
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A comparison of models for cost-sensitive active learning
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Aspects of semi-supervised and active learning in conditional random fields
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
A two-phase hybrid of semi-supervised and active learning approach for sequence labeling
Intelligent Data Analysis
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While Active Learning (AL) has already been shown to markedly reduce the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about the internal structure of the selected sequences (typically, sentences). We propose a semi-supervised AL approach for sequence labeling where only highly uncertain subsequences are presented to human annotators, while all others in the selected sequences are automatically labeled. For the task of entity recognition, our experiments reveal that this approach reduces annotation efforts in terms of manually labeled tokens by up to 60% compared to the standard, fully supervised AL scheme.