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Leveraging Contextual Text Representations for Anonymizing German Financial Documents

Author: D. Biesner, R. Ramamurthy, M. Lübbering, B. Fürst, H. Ismail, L. Hillebrand, A. Ladi, M. Pielka, R. Stenzel, T. Khameneh, V. Krapp, I. Huseynov, J. Schlums, U. Stoll, U. Warning, B. Kliem, C. Bauckhage, R. Sifa
Journal: AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services at KDF
Year: 2020

Citation information

D. Biesner, R. Ramamurthy, M. Lübbering, B. Fürst, H. Ismail, L. Hillebrand, A. Ladi, M. Pielka, R. Stenzel, T. Khameneh, V. Krapp, I. Huseynov, J. Schlums, U. Stoll, U. Warning, B. Kliem, C. Bauckhage, R. Sifa,
AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services at KDF,
2020,
https://www.researchgate.net/publication/347445249_Leveraging_Contextual_Text_Representations_for_Anonymizing_German_Financial_Documents

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 large scale evaluation of different deep learning techniques.