About: Maximum-entropy Markov model

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In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learnt are connected in a Markov chain rather than being conditionally independent of each other. MEMMs find applications in natural language processing, specifically in part-of-speech tagging and information extraction.

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rdf:type	person
rdfs:label	Maximum-entropy Markov model (en)
rdfs:comment	In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learnt are connected in a Markov chain rather than being conditionally independent of each other. MEMMs find applications in natural language processing, specifically in part-of-speech tagging and information extraction. (en)
sameAs	Maximum-entropy Markov model Maximum-entropy Markov model
dbp:wikiPageUsesTemplate	dbt:Citation_needed dbt:Redirect dbt:Reflist
Subject	Statistical natural language processing Markov models
gold:hypernym	Model
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Maximum-entropy_Markov_model?oldid=1000108847&ns=0
Wikipage page ID	27900233 (xsd:integer)
page length (characters) of wiki page	6929 (xsd:nonNegativeInteger)
Wikipage revision ID	1000108847 (xsd:integer)
Link from a Wikipage to another Wikipage	Forward–backward algorithm Baum–Welch algorithm Semi-supervised learning Sequence labeling Statistics Viterbi algorithm Statistical natural language processing Generalized iterative scaling Markov models Discriminative model Conditional independence Conditional random field Hidden Markov model Part-of-speech tagging Graphical model Information extraction Markov chain Maximum entropy probability distribution Multinomial logistic regression Natural language processing
has abstract	In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learnt are connected in a Markov chain rather than being conditionally independent of each other. MEMMs find applications in natural language processing, specifically in part-of-speech tagging and information extraction. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Maximum-entropy_Markov_model
is Wikipage redirect of	Maximum entropy Markov model MEMM Conditional Markov model
is Link from a Wikipage to another Wikipage of	List of statistics articles List of things named after Andrey Markov CMM Generalized iterative scaling Generative model List of probability topics Conditional random field Hidden Markov model Information extraction Markovian discrimination Maximum entropy Markov model MEMM Conditional Markov model
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Maximum-entropy_Markov_model

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