Difference between revisions of "Conditional Random Fields"
PastStudents (talk | contribs) |
PastStudents (talk | contribs) |
||
| Line 2: | Line 2: | ||
== Linear-chain Conditional Random Fields == | == 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: | A CRF on (X, Y) is specified by a local feature vector and a weight vector, the local features are defined as follows: | ||
| Line 21: | Line 21: | ||
The decoding process can be done with the Viterbi algorithm. | The decoding process can be done with the Viterbi algorithm. | ||
| − | |||
== Relevant Papers == | == Relevant Papers == | ||
Revision as of 23:01, 30 November 2010
This is a method discussed in Information Extraction 10-707 in Fall 2010.
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.
