Biesner, DavidDavidBiesnerRamamurthy, RajkumarRajkumarRamamurthyLübbering, MaxMaxLübberingFürst, BenediktBenediktFürstIsmail, H.H.IsmailHillebrand, Lars PatrickLars PatrickHillebrandLadi, AnnaAnnaLadiPielka, MarenMarenPielkaStenzel, RobinRobinStenzelKhameneh, Tim DilmaghaniTim DilmaghaniKhamenehKrapp, V.V.KrappHuseynov, I.I.HuseynovSchlums, J.J.SchlumsStoll, U.U.StollWarning, U.U.WarningKliem, B.B.KliemBauckhage, ChristianChristianBauckhageSifa, RafetRafetSifa2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/408839Despite 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.en005006629conference paper