Machine Learning
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A fast finite-state relaxation method for enforcing global constraints on sequence decoding
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Graphical Models, Exponential Families, and Variational Inference
Graphical Models, Exponential Families, and Variational Inference
Incremental integer linear programming for non-projective dependency parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Dependency parsing by belief propagation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
On the complexity of non-projective data-driven dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Parse, price and cut: delayed column and row generation for graph based parsers
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Monte Carlo MCMC: efficient inference by approximate sampling
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Recently, relaxation approaches have been successfully used for MAP inference on NLP problems. In this work we show how to extend the relaxation approach to marginal inference used in conditional likelihood training, posterior decoding, confidence estimation, and other tasks. We evaluate our approach for the case of second-order dependency parsing and observe a tenfold increase in parsing speed, with no loss in accuracy, by performing inference over a small subset of the full factor graph. We also contribute a bound on the error of the marginal probabilities by a sub-graph with respect to the full graph. Finally, while only evaluated with BP in this paper, our approach is general enough to be applied with any marginal inference method in the inner loop.