Recent work looking to directly model non-local dependencies and do approximate inference are that of Bunescu and Mooney (2004) , who use a Relational Markov Network (RMN) (Taskar et al., 2002) to explicitly model long-distance dependencies, Sutton and McCallum (2004) , who introduce skip-chain CRFs, which add additional non-local edges to the underlying CRF sequence model (which Bunescu and Mooney (2004) lack) and<cite> Finkel et al. (2005)</cite> who hand-set penalties for inconsistency in labels based on the training data and then use Gibbs Sampling for doing approximate inference where the goal is to obtain the label sequence that maximizes the product of the CRF objective function and their penalty.