2005 AnIntroForCRFs

• (Jie Tang, 2005) ⇒ Jie Tang. (2005). “An Introduction for Conditional Random Fields.” In: Literature Survey ¨C, Dec, 2005, at Tsinghua.

Subject Headings: Conditional Random Fields, Literature Survey.

Cited By

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

Quotes

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.

Definition

• 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

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 AnIntroForCRFs	Jie Tang			An Introduction for Conditional Random Fields		Literature Survey ¨C	http://keg.cs.tsinghua.edu.cn/persons/tj/Reports/CRFs-Jie-Tang.ppt			2005