McCallum Bellare and Pereira 2005 A Conditional Random Field for Discriminatively-Trained Finite-State String Edit Distance
Contents
Citation
Andrew McCallum, Kedar Bellare and Fernando Pereira. A Conditional Random Field for Discriminatively-Trained Finite-State String Edit Distance. Conference on Uncertainty in AI (UAI'05), 2005.
Online
Summary
This paper presents a discriminative string edit CRF. Conditional random fields outperform generative models for string edit distance prediction (such as those used in the work of Ristad and Yianilos and Bilenko and Mooney) since they are able to use complex, arbitrary features over the input strings. As in the case of the generative models, this method does not require to specify the edit sequence (alignment) of the training strings, however, it does require negative input instances.
The CRF presented in this paper is based on a finite state machine model with a single start state and two sets of disjoint states representing 'matching' and 'non-matching' string pairs.
