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  4. Leveraging Contextual Text Representations for Anonymizing German Financial Documents
 
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2020
Conference Paper
Title

Leveraging Contextual Text Representations for Anonymizing German Financial Documents

Abstract
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.
Author(s)
Biesner, David  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Ramamurthy, Rajkumar  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Lübbering, Max  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Fürst, Benedikt
PriceWaterhouseCoopers GmbH WPG, Germany
Ismail, H.
Hillebrand, Lars Patrick  
Ladi, Anna  
Pielka, Maren  
Stenzel, Robin
Khameneh, Tim Dilmaghani
Krapp, V.
Huseynov, I.
Schlums, J.
Stoll, U.
Warning, U.
Kliem, B.
Bauckhage, Christian  
Sifa, Rafet  
Mainwork
AAAI-2020 Workshop on Knowledge Discovery from Unstructured Data in Financial Services. Accepted Papers. Online resource  
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
Conference on Artificial Intelligence (AAAI) 2020  
Workshop on Knowledge Discovery from Unstructured Data in Financial Services 2020  
Link
Link
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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