2005 AnIntroForCRFs

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Subject Headings: Conditional Random Fields, Literature Survey.


Cited By

~261 http://scholar.google.com/scholar?cites=1064203942494716171


Hidden Markov Model

  • Cannot represent multiple interacting features or long range dependences between observed elements.

Maximum Entropy Markov Model

  • Label bias problem: the probability transitions leaving any given state must sum to one

Conditional Random Field

  • undirected graphical model globally conditioned on X
  • Given an undirected graph G=(V, E) such that Y={Yv|v∈V}, if

  • the probability of Yv given X and those random variables corresponding to nodes neighboring v in G. Then (X, Y) is a conditional random field.


  • CRF is a Markov Random Fields.
  • By the Hammersley-Clifford theorem, the probability of a label can be expressed as a Gibbs distribution, so that

  • What is clique?
  • By only taking consideration of the one node and two nodes cliques, we have

In Labeling

  • In labeling, the task is to find the label sequence that has the largest probability
  • Then the key is to estimate the parameter lambda



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2005 AnIntroForCRFsJie TangAn Introduction for Conditional Random FieldsLiterature Survey ¨Chttp://keg.cs.tsinghua.edu.cn/persons/tj/Reports/CRFs-Jie-Tang.ppt2005