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Record linkage aims at identifying duplicate records across datasets. Most existing record linkage techniques have been designed for monolingual datasets. In this paper, we propose a novel approach, CLRL, that links the records in a cross-language setting, where each input dataset is in a different language. CLRL combines monolingual similarity measures with multilingual cross-language word embedding similarities to identify the correspondence of records across datasets. As our experiments show, CLRL outperforms baseline approaches in cross-language data integration settings.

CLRL - Feature Engineering for Cross-Language Record Linkage.