Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Discriminative language modeling with conditional random fields and the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Logarithmic opinion pools for conditional random fields
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Logarithmic opinion pools for conditional random fields
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Scaling conditional random fields by one-against-the-other decomposition
Journal of Computer Science and Technology
MELB-YB: preposition sense disambiguation using rich semantic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Revisiting output coding for sequential supervised learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semantic role labelling with tree conditional random fields
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A Unified Character-Based Tagging Framework for Chinese Word Segmentation
ACM Transactions on Asian Language Information Processing (TALIP)
Efficient inference in large conditional random fields
ECML'06 Proceedings of the 17th European conference on Machine Learning
ACM Transactions on Asian Language Information Processing (TALIP)
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Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks. However, these tasks have mostly involved very small label sets. When deployed on tasks with larger label sets, the requirements for computational resources mean that training becomes intractable.This paper describes a method for training CRFs on such tasks, using error correcting output codes (ECOC). A number of CRFs are independently trained on the separate binary labelling tasks of distinguishing between a subset of the labels and its complement. During decoding, these models are combined to produce a predicted label sequence which is resilient to errors by individual models.Error-correcting CRF training is much less resource intensive and has a much faster training time than a standardly formulated CRF, while decoding performance remains quite comparable. This allows us to scale CRFs to previously impossible tasks, as demonstrated by our experiments with large label sets.