Bayesian regularization and pruning using a Laplace prior
Neural Computation
Selecting weighting factors in logarithmic opinion pools
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth 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
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
Evaluation and extension of maximum entropy models with inequality constraints
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
Logarithmic opinion pools for conditional random fields
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Concensus of self-features for nonverbal behavior analysis
HBU'10 Proceedings of the First international conference on Human behavior understanding
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Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation when applying these models to real-world NLP data sets. Conventional approaches to regularising CRFs has focused on using a Gaussian prior over the model parameters. In this paper we explore other possibilities for CRF regularisation. We examine alternative choices of prior distribution and we relax the usual simplifying assumptions made with the use of a prior, such as constant hyperparameter values across features. In addition, we contrast the effectiveness of priors with an alternative, parameter-free approach. Specifically, we employ logarithmic opinion pools (LOPs). Our results show that a LOP of CRFs can outperform a standard unregularised CRF and attain a performance level close to that of a regularised CRF, without the need for intensive hyperparameter search.