Paper: On the Use of Virtual Evidence in Conditional Random Fields

ACL ID D09-1134
Title On the Use of Virtual Evidence in Conditional Random Fields
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors
  • Xiao Li (Microsoft Research, Redmond WA)

Virtual evidence (VE), first introduced by (Pearl, 1988), provides a convenient way of incorporating prior knowledge into Bayesian networks. This work general- izes the use of VE to undirected graph- ical models and, in particular, to condi- tional random fields (CRFs). We show that VE can be naturally encoded into a CRF model as potential functions. More importantly, we propose a novel semi- supervised machine learning objective for estimating a CRF model integrated with VE. The objective can be optimized us- ing the Expectation-Maximization algo- rithm while maintaining the discriminative nature of CRFs. When evaluated on the CLASSIFIEDS data, our approach signif- icantly outperforms the best known solu- tions reported on this task.