Maximum-entropy Markov model

<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a <a href="/facts/Graphical_model/XxfmKhmM">graphical model</a> for <a href="/facts/Sequence_labeling/yrQ0QTaQ">sequence labeling</a> that combines features of <a href="/facts/Hidden_Markov_model/ur1zTAhP">hidden Markov models</a> (HMMs) and <a href="/facts/Maximum_entropy_probability_distribution/MVwFzu49">maximum entropy</a> (MaxEnt) models. An MEMM is a <a href="/facts/Discriminative_model/hPGxBneP">discriminative model</a> that extends a standard <a href="/facts/Maximum_entropy_classifier/aeF5lqDE">maximum entropy classifier</a> by assuming that the unknown values to be learnt are connected in a <a href="/facts/Markov_chain/5oP5hntk">Markov chain</a> rather than being <a href="/facts/Conditionally_independent/udUXFaMw">conditionally independent</a> of each other. MEMMs find applications in <a href="/facts/Natural_language_processing/1hjMKsSN">natural language processing</a>, specifically in <a href="/facts/Part-of-speech_tagging/BYAPmH3r">part-of-speech tagging</a> and <a href="/facts/Information_extraction/DL9nMD2X">information extraction</a>.
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Maximum-entropy Markov model open-in-new

Maximum-entropy Markov model