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Maximum-entropy Markov model

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In machine learning, a maximum entropy Markov model (MEMM) is a graphical model for sequence labeling that combine features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. MEMMs find applications in natural language processing, specifically in information extraction.

Linear chain conditional random fields were designed to overcome certain weaknesses of MEMMs.

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