Difference between revisions of "Smith and Eisner 2005:Contrastive Estimation: Training Log-Linear Models on Unlabeled Data"

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== Summary ==
 
== Summary ==
  
This is an interesting [[Category::paper]] that presents an unsupervised [[UsesMethod::Contrastive Estimation]] method for [[UsesMethod::Conditional Random Fields]] and other [[AddressesProblem::Log-Linear Models]], which can be easily applied to estimation problems in [[AddressesProblem::Part of Speech Tagging]], [[AddressesProblem::Named Entity Recognition]] and [[AddressesProblem::Semantic Role Labeling]]. When applying this technique to POS tagging, the observed results outperforms [[Expectation Maximization]], and is robust when the dictionary quality is poor.
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This is an interesting [[Category::paper]] that presents an unsupervised [[UsesMethod::Contrastive Estimation]] method for [[UsesMethod::Conditional Random Fields]] and other [[AddressesProblem::Log-Linear Models]], which can be easily applied to estimation problems in [[AddressesProblem::Part of Speech Tagging]], [[AddressesProblem::Named Entity Recognition]] and [[AddressesProblem::Semantic Role Labeling]]. When applying this technique to POS tagging, the observed results outperforms [[UsesMethod::Expectation Maximization]], and is robust when the dictionary quality is poor.
  
 
== Brief description of the method ==
 
== Brief description of the method ==

Revision as of 21:52, 29 September 2011

Citation

Smith, Noah A. and Jason Eisner (2005). Contrastive estimation: Training log-linear models on unlabeled data. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 354-362, Ann Arbor, Michigan, June.

Online version

Smith and Eisner 2005

Summary

This is an interesting paper that presents an unsupervised Contrastive Estimation method for Conditional Random Fields and other Log-Linear Models, which can be easily applied to estimation problems in Part of Speech Tagging, Named Entity Recognition and Semantic Role Labeling. When applying this technique to POS tagging, the observed results outperforms Expectation Maximization, and is robust when the dictionary quality is poor.

Brief description of the method

Experimental Result

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