# 9.6: Conditional Random Fields

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Conditional Random Fields, CRFs, are an alternative to HMMs. Being a discriminative approach, this type of model doesnt take into account the joint distribution of everything, as does a poorly scaling HMM. The hidden states in a CRF are conditioned on the input sequence. (See Figure 9.8)3

A feature function is like a score, returning a real-valued number as a function of its inputs that reflects the evidence for a label at a particular position. (See Figure 9.9) The conditional probability of the emitted sequence is its score divided by the total score of the hidden state. (See Figure 9.10)