2010 EfficientGraphbasedSemiSupervis

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

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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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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2010 EfficientGraphbasedSemiSupervisAmarnag Subramanya
Slav Petrov
Fernando Pereira
Efficient Graph-based Semi-supervised Learning of Structured Tagging Models2010
AuthorAmarnag Subramanya +, Slav Petrov + and Fernando Pereira +
titleEfficient Graph-based Semi-supervised Learning of Structured Tagging Models +
year2010 +