The standard training method of Conditional Random Fields (CRFs) is very slow for large-scale applications. As an alternative, piecewise training divides the full graph into pieces, trains them independently, and combines the learned weights at test time. In this paper, we present \emph{separate} training for undirected models based on the novel Co-occurrence Rate Factorization (CR-F). Separate training is a local training method. In contrast to MEMMs, separate training is unaffected by the label bias problem. Experiments show that separate training (i) is unaffected by the label bias problem; (ii) reduces the training time from weeks to seconds; and (iii) obtains competitive results to the standard and piecewise training on linear-chain CRFs.
@article{arxiv.1008.1566,
title = {Separate Training for Conditional Random Fields Using Co-occurrence Rate Factorization},
author = {Zhemin Zhu and Djoerd Hiemstra and Peter Apers and Andreas Wombacher},
journal= {arXiv preprint arXiv:1008.1566},
year = {2012}
}