Conditional Random Field

Conditional Random Fields were developed to model dependencies among neighboring labels in sequence-labeling tasks. They have historically been important in named entity recognition, part-of-speech tagging, and biological sequence analysis. CRFs combine local observation features with global label consistency. As a result, they can produce more structured predictions than independent classifiers.