Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Diagram Structure Recognition by Bayesian Conditional Random Fields
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Chinese segmentation and new word detection using conditional random fields
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Subword-based tagging by conditional random fields for Chinese word segmentation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Training conditional random fields using virtual evidence boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Integrating unsupervised and supervised word segmentation: The role of goodness measures
Information Sciences: an International Journal
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Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational machines, trained CRFs Models for a real application quickly inflate. Recently, researchers often have to use models with tens of millions features. This paper considers pruning an existing CRFs model for storage reduction and decoding speedup. We propose a simple but efficient rank metric for feature group rather than features that previous work usually focus on. A series of experiments in two typical labeling tasks, word segmentation and named entity recognition for Chinese, are carried out to check the effectiveness of the proposed method. The results are quite positive and show that CRFs models are highly redundant, even using carefully selected label set and feature templates.