Asymptotic Theory for Linear-Chain Conditional Random Fields
Abstract
In this theoretical paper we develop an asymptotic theory for Linear-Chain Conditional Random Fields (L-CRFs) and apply it to derive conditions under which the Maximum Likelihood Estimates (MLEs) of the model weights are strongly consistent. We first define L-CRFs for infinite sequences and analyze some of their basic properties. Then we establish conditions under which ergodicity of the observations implies ergodicity of the joint sequence of observations and labels. This result is the key ingredient to derive conditions for strong consistency of the MLEs. Interesting findings are that the consistency crucially depends on the limit behavior of the Hessian of the likelihood function and that, asymptotically, the state feature functions do not matter.
Cite
Text
Sinn and Poupart. "Asymptotic Theory for Linear-Chain Conditional Random Fields." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Sinn and Poupart. "Asymptotic Theory for Linear-Chain Conditional Random Fields." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/sinn2011aistats-asymptotic/)BibTeX
@inproceedings{sinn2011aistats-asymptotic,
title = {{Asymptotic Theory for Linear-Chain Conditional Random Fields}},
author = {Sinn, Mathieu and Poupart, Pascal},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {679-687},
volume = {15},
url = {https://mlanthology.org/aistats/2011/sinn2011aistats-asymptotic/}
}