# 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.

## Notes

## 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

,

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 |

Facts about "2005 AnIntroForCRFs"

Author | Jie Tang + |

journal | Literature Survey ¨C + |

title | An Introduction for Conditional Random Fields + |

titleUrl | http://keg.cs.tsinghua.edu.cn/persons/tj/Reports/CRFs-Jie-Tang.ppt + |

year | 2005 + |