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• W1761401273 abstract "The task of matching co-referent records is known among other names as record linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonably 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 record-linkage 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 monotonicity 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." @default.
• W1761401273 created "2016-06-24" @default.
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• W1761401273 date "2004-07-07" @default.
• W1761401273 modified "2023-09-28" @default.
• W1761401273 title "A hierarchical graphical model for record linkage" @default.
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• W1761401273 doi "https://doi.org/10.5555/1036843.1036898" @default.
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