Paper: Regularisation Techniques for Conditional Random Fields: Parameterised Versus Parameter-Free

ACL ID I05-1078
Title Regularisation Techniques for Conditional Random Fields: Parameterised Versus Parameter-Free
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation when applying these models to real-world NLP data sets. Conventional approaches to regularising CRFs has focused on using a Gaussian prior over the model parameters. In this paper we explore other possibilities for CRF regularisation. We examine alternative choices of prior distribution and we relax the usual simplifying assumptions made with the use of a prior, such as constant hyperparameter values across features. In addition, we contrast the effec- tiveness of priors with an alternative, parameter-free approach. Specifi- cally, we employ logarithmic opinion pools (LOPs). Our results show that a LOP of CRFs can outperform a standard unregularised CRF and attain a performance level close to tha...