- (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.
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.
|2010 EfficientGraphbasedSemiSupervis||Amarnag Subramanya|
|Efficient Graph-based Semi-supervised Learning of Structured Tagging Models||2010|