- Abstract:
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.
- Copyright:
- 2006 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- No links available
- Bibtex format
- @InProceedings{EDI-INF-RR-0722,
- author = {
Andrew Smith
and Trevor Cohn
and Miles Osborne
},
- title = {Logarithmic Opinion Pools for Conditional Random Fields},
- book title = {Proceedings of ACL 2005 (Meeting of the Association for Computational Linguistics)},
- year = 2005,
- month = {Jun},
- pages = {18-25},
- }
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