Difference between revisions of "Contrastive Estimation"
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This is a [[Category::method]] proposed by [[RelatedPaper::Smith and Eisner 2005:Contrastive Estimation: Training Log-Linear Models on Unlabeled Data]]. | This is a [[Category::method]] proposed by [[RelatedPaper::Smith and Eisner 2005:Contrastive Estimation: Training Log-Linear Models on Unlabeled Data]]. | ||
| − | The proposed approach deals with the estimation of log-linear models (e.g. [[AddressesProblem::Conditional Random Fields]]) in an unsupervised fashion. The method focuses on the denominator <math> \sum_{x\prime,y\prime} p (x\prime,y\prime)</math> of the log-linear models by exploiting the so called ''implicit negative evidence''. | + | The proposed approach deals with the estimation of log-linear models (e.g. [[AddressesProblem::Conditional Random Fields]]) in an unsupervised fashion. The method focuses on the denominator <math> \sum_{x\prime,y\prime} p (x\prime,y\prime)</math> of the log-linear models by exploiting the so called ''implicit negative evidence'' in the probability mass. |
== Motivation == | == Motivation == | ||
Revision as of 16:04, 29 September 2011
This is a method proposed by Smith and Eisner 2005:Contrastive Estimation: Training Log-Linear Models on Unlabeled Data.
The proposed approach deals with the estimation of log-linear models (e.g. Conditional Random Fields) in an unsupervised fashion. The method focuses on the denominator of the log-linear models by exploiting the so called implicit negative evidence in the probability mass.
