Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Integer linear programming inference for conditional random fields
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Practical very large scale CRFs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Temporal maximum margin Markov network
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
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In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse Higher Order Conditional Random Fields (SHO-CRFs), which are able to handle local features and sparse higher order features together using a novel tractable exact inference algorithm. Our main insight is that states and transitions with same potential functions can be grouped together, and inference is performed on the grouped states and transitions. Though the complexity is not polynomial, SHO-CRFs are still efficient in practice because of the feature sparseness. Experimental results on optical character recognition and Chinese organization name recognition show that with the same higher order feature set, SHO-CRFs significantly outperform previous approaches.