Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A general method for reducing the complexity of relational inference and its application to MCMC
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Collective cross-document relation extraction without labelled data
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Knowledge-based weak supervision for information extraction of overlapping relations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Large-scale cross-document coreference using distributed inference and hierarchical models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Conditional random fields and other graphical models have achieved state of the art results in a variety of NLP and IE tasks including coreference and relation extraction. Increasingly, practitioners are using models with more complex structure---higher tree-width, larger fanout, more features, and more data---rendering even approximate inference methods such as MCMC inefficient. In this paper we propose an alternative MCMC sampling scheme in which transition probabilities are approximated by sampling from the set of relevant factors. We demonstrate that our method converges more quickly than a traditional MCMC sampler for both marginal and MAP inference. In an author coreference task with over 5 million mentions, we achieve a 13 times speedup over regular MCMC inference.