Difference between revisions of "Conditional Random Fields"
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This is a [[category::method]] discussed in [[Information Extraction 10-707 in Fall 2010]]. | This is a [[category::method]] discussed in [[Information Extraction 10-707 in Fall 2010]]. | ||
| − | Conditional random field (CRF) is a type of discriminative probabilistic model most often used for the labeling or parsing of sequential data, such as natural language text or biological sequences. With a foundation from Maximum Entropy model and Hidden Markov model, it outperforms them in particular on the tasks of [[AddressesProblem: | + | Conditional random field (CRF) is a type of discriminative probabilistic model most often used for the labeling or parsing of sequential data, such as natural language text or biological sequences. With a foundation from Maximum Entropy model and Hidden Markov model, it outperforms them in particular on the tasks of [[AddressesProblem::Shallow Parsing]], [[AddressesProblem::Named Entity Recognition]] and [[AddressesProblem::visual object recognition]] etc. |
== Introduction == | == Introduction == | ||
Revision as of 23:05, 30 November 2010
This is a method discussed in Information Extraction 10-707 in Fall 2010.
Conditional random field (CRF) is a type of discriminative probabilistic model most often used for the labeling or parsing of sequential data, such as natural language text or biological sequences. With a foundation from Maximum Entropy model and Hidden Markov model, it outperforms them in particular on the tasks of Shallow Parsing, Named Entity Recognition and visual object recognition etc.
Introduction
Linear-chain Conditional Random Fields
One of the commonly used version is the linear-chain conditional random fields. Such CRFs define conditional probability distributions p(Y|X) of label sequences given input sequences. The label and input sequences are assumed to have the same length.
A CRF on (X, Y) is specified by a local feature vector and a weight vector, the local features are defined as follows:
And the global feature vector is thus an aggregate of the local features:
The conditional probability distribution defined by CRF is then defined as
In sequential labeling task, we would like to find the most probable label sequence for input sequence, thus we use the following formula
The decoding process can be done with the Viterbi algorithm.
