2010 EfficientGraphbasedSemiSupervis

• (Subramanya et al., 2010) ⇒ Amarnag Subramanya, Slav Petrov, and Fernando Pereira. (2010). “Efficient Graph-based Semi-supervised Learning of Structured Tagging Models.” In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing.

Subject Headings: Graph-based Semi-Supervised Learning Algorithm, Structured Tagging Task.

Cited By

• http://scholar.google.com/scholar?q=%22Efficient+graph-based+semi-supervised+learning+of+structured+tagging+models%22+2010
• http://dl.acm.org/citation.cfm?id=106167720&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 EfficientGraphbasedSemiSupervis	Fernando Pereira Amarnag Subramanya Slav Petrov			Efficient Graph-based Semi-supervised Learning of Structured Tagging Models						2010