Higher-order CRFs and Semi-Markov CRFs
CRFs can be extended into higher order models by making each dependent on a fixed number of previous variables . Training and inference are only practical for small values of (such as o ≤ 5), since their computational cost increases exponentially with . Large-margin models for structured prediction, such as the structured Support Vector Machine can be seen as an alternative training procedure to CRFs.
There exists another generalization of CRFs, the semi-Markov conditional random field (semi-CRF), which models variable-length segmentations of the label sequence . This provides much of the power of higher-order CRFs to model long-range dependencies of the, at a reasonable computational cost.
Read more about this topic: Conditional Random Field, Description