Conditional Random Fields (CRFs) are known to have computational efficiency issues relative to the related Hidden Markov Model (HMM) and Maximum-Entropy Markov Model (MEMM) models. (In particular, I assume you are referring to linear-chain CRFs which are appropriate for sequence labeling.)

CRFs were developed as an adjustment to MEMMs which in turn were created as a discriminative analogue of the generative HMM model.

The main difference between (linear-chain) CRFs and MEMMs is that CRFs use global normalization. The cost of calculating this term scales quadratically in the number of label classes.

I am not sure if this is reflected in any practical use cases where HMMs would be preferred, but it is one theoretical disadvantage of CRFs.

Source: Awni Hannun (2019) The Label Bias Problem

Conditional Random Fields (CRFs) are known to have computational efficiency issues relative to the related Hidden Markov Model (HMM) and Maximum-Entropy Markov Model (MEMM) models. (In particular, I assume you are referring to linear-chain CRFs which are appropriate for sequence labeling.)

CRFs were developed as an adjustment to MEMMs which in turn were created as a discriminative analogue of the generative HMM model.

The main difference between (linear-chain) CRFs and MEMMs is that CRFs use global normalization. The cost of calculating this term scales quadratically in the number of label classes.

I am not sure if this is reflected in any practical use cases where HMMs would be preferred, but it is one theoretical disadvantage of CRFs.

Source: Awni Hannun (2019) The Label Bias Problem

Some related pieces on this topic are this series by David S. Batista:

• https://www.davidsbatista.net/blog/2017/11/11/HHM_and_Naive_Bayes/

• https://www.davidsbatista.net/blog/2017/11/12/Maximum_Entropy_Markov_Model/

• https://www.davidsbatista.net/blog/2017/11/11/HHM_and_Naive_Bayes/

CRFs are known to have computational efficiency issues relative to the related HMM and MEMM models. (In particular, I assume you are referring to linear-chain CRFs which are appropriate for sequence labeling.)

CRFs were developed as an adjustment to MEMMs which in turn were created as a discriminative analogue of the generative HMM model.

The main difference between (linear-chain) CRFs and MEMMs is that CRFs use global normalization. The cost of calculating this term scales quadratically in the number of label classes.

I am not sure if this is reflected in any practical use cases where HMMs would be preferred, but it is one theoretical disadvantage of CRFs.

Source: Awni Hannun (2019) The Label Bias Problem

Conditional Random Fields (CRFs) are known to have computational efficiency issues relative to the related Hidden Markov Model (HMM) and Maximum-Entropy Markov Model (MEMM) models. (In particular, I assume you are referring to linear-chain CRFs which are appropriate for sequence labeling.)

CRFs were developed as an adjustment to MEMMs which in turn were created as a discriminative analogue of the generative HMM model.

The main difference between (linear-chain) CRFs and MEMMs is that CRFs use global normalization. The cost of calculating this term scales quadratically in the number of label classes.

I am not sure if this is reflected in any practical use cases where HMMs would be preferred, but it is one theoretical disadvantage of CRFs.

Source: Awni Hannun (2019) The Label Bias Problem