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Title:Logarithmic Opinion Pools for Conditional Random Fields
Authors: Andrew Smith ; Trevor Cohn ; Miles Osborne
Date:Jun 2005
Publication Title:Proceedings of ACL 2005 (Meeting of the Association for Computational Linguistics)
Publication Type:Conference Paper Publication Status:Published
Page Nos:18-25
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
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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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