Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

9-30-2020

Journal or Book Title

arXiv

Abstract

By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in text mining to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is scalable and fully automatic, unlike the classical approach that generally requires clerical review to resolve the undecided cases.

Comments

This preprint is made available through arXiv: https://arxiv.org/abs/2009.14797.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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