Combining labeled and unlabeled data with co-training
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Part-of-speech tagging from 97% to 100%: is it time for some linguistics?
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Unsupervised part-of-speech tagging with bilingual graph-based projections
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Semi-supervised frame-semantic parsing for unknown predicates
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Semi-supervised multi-task learning of structured prediction models for web information extraction
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Unsupervised semantic role induction with graph partitioning
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Graph-based lexicon expansion with sparsity-inducing penalties
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Graph-based semi-supervised learning algorithms for NLP
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Bootstrapping via graph propagation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
A cost sensitive part-of-speech tagging: differentiating serious errors from minor errors
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Improved parsing and POS tagging using inter-sentence consistency constraints
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Semi-supervised multiple instance learning based domain adaptation for object detection
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Wikipedia entity expansion and attribute extraction from the web using semi-supervised learning
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We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to part-of-speech (POS) tagging. The algorithm uses a similarity graph to encourage similar n-grams to have similar POS tags. We demonstrate the efficacy of our approach on a domain adaptation task, where we assume that we have access to large amounts of unlabeled data from the target domain, but no additional labeled data. The similarity graph is used during training to smooth the state posteriors on the target domain. Standard inference can be used at test time. Our approach is able to scale to very large problems and yields significantly improved target domain accuracy.