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arXiv · 1008.1566

Separate Training for Conditional Random Fields Using Co-occurrence Rate Factorization

Zhemin Zhu·Djoerd Hiemstra·Peter Apers·Andreas Wombacher
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Abstract

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.

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Zhemin Zhu, Djoerd Hiemstra, Peter Apers, Andreas Wombacher. 2012-12-04. Separate Training for Conditional Random Fields Using Co-occurrence Rate Factorization. https://arxiv.org/abs/1008.1566

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