Sciweavers

Share
EMNLP
2009

On the Use of Virtual Evidence in Conditional Random Fields

11 years 3 months ago
On the Use of Virtual Evidence in Conditional Random Fields
Virtual evidence (VE), first introduced by (Pearl, 1988), provides a convenient way of incorporating prior knowledge into Bayesian networks. This work generalizes the use of VE to undirected graphical models and, in particular, to conditional random fields (CRFs). We show that VE can be naturally encoded into a CRF model as potential functions. More importantly, we propose a novel semisupervised machine learning objective for estimating a CRF model integrated with VE. The objective can be optimized using the Expectation-Maximization algorithm while maintaining the discriminative nature of CRFs. When evaluated on the CLASSIFIEDS data, our approach significantly outperforms the best known solutions reported on this task.
Xiao Li
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EMNLP
Authors Xiao Li
Comments (0)
books