Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
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Unsupervised word sense disambiguation rivaling supervised methods
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ICML '05 Proceedings of the 22nd international conference on Machine learning
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ICML '05 Proceedings of the 22nd international conference on Machine learning
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ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
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Effective self-training for parsing
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Learning and inference with constraints
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A risk minimization framework for domain adaptation
Proceedings of the 18th ACM conference on Information and knowledge management
Combining labeled and unlabeled data with word-class distribution learning
Proceedings of the 18th ACM conference on Information and knowledge management
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Self-training from labeled features for sentiment analysis
Information Processing and Management: an International Journal
Localized factor models for multi-context recommendation
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We present an algorithmic framework for learning multiple related tasks. Our framework exploits a form of prior knowledge that relates the output spaces of these tasks. We present PAC learning results that analyze the conditions under which such learning is possible. We present results on learning a shallow parser and named-entity recognition system that exploits our framework, showing consistent improvements over baseline methods.