Logarithmic Opinion Pools for Conditional Random Fields
AuthorSMITH, ANDREW; COHN, TREVOR; OSBORNE, MILES
Source TitleProceedings, 43rd Annual Meeting of the Association for Computational Linguists
University of Melbourne Author/sCohn, Trevor
AffiliationEngineering: Department of Computer Science and Software Engineering
Document TypeConference Paper
CitationsSmith, A., Cohn, T. & Osborne, M. (2005). Logarithmic Opinion Pools for Conditional Random Fields. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguists, Ann Arbor, Michigan.
Access StatusOpen Access
Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation to counter the tendency of these models to overfit. The standard approach to regularising CRFs involves a prior distribution over the model parameters, typically requiring search over a hyperparameter space. In this paper we address the overfitting problem from a different perspective, by factoring the CRF distribution into a weighted product of individual 'expert' CRF distributions. We call this model a logarithmic opinion pool (LOP) of CRFs (LOP-CRFs). We apply the LOP-CRF to two sequencing tasks. Our results show that unregularised expert CRFs with an unregularised CRF under a LOP can outperform the unregularised CRF, and attain a performance level close to the regularised CRF. LOP-CRFs therefore provide a viable alternative to CRF regularisation without the need for hyperparameter search.
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