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dc.contributor.authorSMITH, ANDREWen_US
dc.contributor.authorCOHN, TREVORen_US
dc.contributor.authorOSBORNE, MILESen_US
dc.date.accessioned2014-05-22T10:18:59Z
dc.date.available2014-05-22T10:18:59Z
dc.date.issued2005en_US
dc.date.submitted2005-05-23en_US
dc.identifier.citationSmith, 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.en_US
dc.identifier.urihttp://hdl.handle.net/11343/33832
dc.description.abstractRecent 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.en_US
dc.formatapplication/pdfen_US
dc.languageengen_US
dc.titleLogarithmic Opinion Pools for Conditional Random Fieldsen_US
dc.typeConference Paperen_US
melbourne.peerreviewPeer Revieweden_US
melbourne.affiliation.departmentEngineering: Department of Computer Science and Software Engineeringen_US
melbourne.source.titleProceedings, 43rd Annual Meeting of the Association for Computational Linguistsen_US
melbourne.source.locationconferenceAnn Arbor, Michiganen_US
dc.description.sourcedateJune, 2005en_US
melbourne.elementsidNA
melbourne.contributor.authorCohn, Trevor
melbourne.accessrightsOpen Access


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