Probabilistic Record Linkage Using Pretrained Text Embeddings

Abstract Pretrained text embeddings are a fast and scalable method for determining whether two texts have similar meaning, capturing not only lexical similarity, but semantic similarity as well. In this article, I show how to incorporate these measures into a probabilistic record linkage procedure that yields considerable improvements in both precision and recall over existing methods. The procedure even allows researchers to link datasets across different languages. I validate the approach with a series of political science applications, and provide open-source statistical software for researchers to efficiently implement the proposed method.