Data-driven name reduction for record linkage

Abstract

Automatic record linkage of data containing personal names is difficult in the presence of name variation and spelling errors. This paper presents a standardization procedure for personal names to address the variation problem. A classification tree based model is constructed using a training set of 65,002 name-variant pairs. The method provides an efficient procedure for record linkage (3500 records per second, F-measure 0.96 on a sample of Dutch historical civil records). The results include links with large edit distance between the records, however recall is lower for this category. A bootstrapping procedure is used to improve recall.