  The task of matching co-referent records is known among other names as rocord
linkage. For large record-linkage problems, often there is little or no labeled
data available, but unlabeled data shows a reasonable clear structure. For such
problems, unsupervised or semi-supervised methods are preferable to supervised
methods. In this paper, we describe a hierarchical graphical model framework
for the linakge-problem in an unsupervised setting. In addition to proposing
new methods, we also cast existing unsupervised probabilistic record-linkage
methods in this framework. Some of the techniques we propose to minimize
overfitting in the above model are of interest in the general graphical model
setting. We describe a method for incorporating monotinicity constraints in a
graphical model. We also outline a bootstrapping approach of using
"single-field" classifiers to noisily label latent variables in a hierarchical
model. Experimental results show that our proposed unsupervised methods perform
quite competitively even with fully supervised record-linkage methods.
