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
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Support Vector Learning for Semantic Argument Classification
Machine Learning
Integer linear programming inference for conditional random fields
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fast inference and learning in large-state-space HMMs
ICML '05 Proceedings of the 22nd international conference on Machine learning
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Discriminative language modeling with conditional random fields and the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Scaling conditional random fields using error-correcting codes
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Multi-domain spoken language understanding with transfer learning
Speech Communication
Efficient inference of CRFs for large-scale natural language data
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Efficient staggered decoding for sequence labeling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Practical very large scale CRFs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Iterative viterbi A* algorithm for k-best sequential decoding
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Hi-index | 0.00 |
Conditional Random Fields (CRFs) are widely known to scale poorly, particularly for tasks with large numbers of states or with richly connected graphical structures. This is a consequence of inference having a time complexity which is at best quadratic in the number of states. This paper describes a novel parameterisation of the CRF which ties the majority of clique potentials, while allowing individual potentials for a subset of the labellings. This has two beneficial effects: the parameter space of the model (and thus the propensity to over-fit) is reduced, and the time complexity of training and decoding becomes sub-quadratic. On a standard natural language task, we reduce CRF training time four-fold, with no loss in accuracy. We also show how inference can be performed efficiently in richly connected graphs, in which current methods are intractable.