Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
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
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth 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
Bootstrapping statistical parsers from small datasets
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Applying Co-Training to reference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Example selection for bootstrapping statistical parsers
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Combining sample selection and error-driven pruning for machine learning of coreference rules
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Adaptive information extraction
ACM Computing Surveys (CSUR)
Classifier combination techniques applied to coreference resolution
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
The problem of ontology alignment on the web: a first report
WAC '06 Proceedings of the 2nd International Workshop on Web as Corpus
Assessing the benefits of partial automatic pre-labeling for frame-semantic annotation
ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
A new co-training-style random forest for computer aided diagnosis
Journal of Intelligent Information Systems
Bootstrapping coreference resolution using word associations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The unsymmetrical-style co-training
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Language Resources and Evaluation
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Successful application of multi-view co-training algorithms relies on the ability to factor the available features into views that are compatible and uncorrelated. This can potentially preclude their use on problems such as coreference resolution that lack an obvious feature split. To bootstrap coreference classifiers, we propose and evaluate a single-view weakly supervised algorithm that relies on two different learning algorithms in lieu of the two different views required by co-training. In addition, we investigate a method for ranking unlabeled instances to be fed back into the bootstrapping loop as labeled data, aiming to alleviate the problem of performance deterioration that is commonly observed in the course of bootstrapping.