Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/274537 
Year of Publication: 
2023
Series/Report no.: 
SAFE Working Paper No. 398
Publisher: 
Leibniz Institute for Financial Research SAFE, Frankfurt a. M.
Abstract: 
In this study, we introduce a novel entity matching (EM) framework. It com-bines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are preva-lent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encod-ing is constructed using (edit) distances instead of semantic similarities, it avoids out-of-vocabulary problems when matching dirty data. We highlight this property by applying an EM application to dirty financial firm-level data extracted from historical archives.
Subjects: 
Entity matching
Entity resolution
Database linking
Machine learning
Record resolution
Similarity encoding
JEL: 
C8
Document Type: 
Working Paper

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