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PublicationAnonymization of German financial documents using neural network-based language models with contextual word representations( 2022-03)
;Loitz, Rüdiger ;Stenzel, RobinThe automatization and digitalization of business processes have led to an increase in the need for efficient information extraction from business documents. However, financial and legal documents are often not utilized effectively by text processing or machine learning systems, partly due to the presence of sensitive information in these documents, which restrict their usage beyond authorized parties and purposes. To overcome this limitation, we develop an anonymization method for German financial and legal documents using state-of-the-art natural language processing methods based on recurrent neural nets and transformer architectures. We present a web-based application to anonymize financial documents and a large-scale evaluation of different deep learning techniques. -
PublicationLeveraging Contextual Text Representations for Anonymizing German Financial Documents( 2020)
;Fürst, Benedikt ;Ismail, H. ;Stenzel, Robin ;Khameneh, Tim Dilmaghani ;Krapp, V. ;Huseynov, I. ;Schlums, J. ;Stoll, U. ;Warning, U. ;Kliem, B.Despite the high availability of financial and legal documents they are often not utilized by text processing or machine learning systems, even though the need for automated processing and extraction of useful patterns from these documents is increasing. This is partly due to the presence of sensitive entities in these documents, which restrict their usage beyond authorized parties and purposes. To overcome this limitation, we consider the task of anonymization in financial and legal documents using state-of-the-art natural language processing methods. Towards this, we present a web-based application to anonymize financial documents and also a largescale evaluation of different deep learning techniques.