Abstract
Legal systems form the foundation of democratic states. Nevertheless, it is nearly impossible for individuals to extract specific information from comprehensive legal documents. We present a human-centered and AI-supported system for semantic question answering (QA) in the German legal domain. Our system is built on top of human collaboration and natural language processing (NLP)-based legal information retrieval. Laypersons and legal professionals re ceive information supporting their research and decision-making by collaborating with the system and its underlying AI methods to enable a smarter society. The internal AI is based on state-of-the-art methods evaluating complex search terms, considering words and phrases specific to German law. Subsequently, relevant documents or answers are ranked and graphically presented to the human. In ad dition to the novel system, we publish the first annotated data set for QA in the German legal domain. The experimental results indicate that our semantic QA workflow outperforms existing approaches.
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Acknowledgements
We would like to thank Nicole Salvi for her support of the project by contributing to the annotation of the published dataset and the literature review conducted, as well as providing her excellent knowledge of the legal sector.
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Hoppe, C., Migenda, N., Pelkmann, D., Hötte, D., Schenck, W. (2022). Collaborative System for Question Answering in German Case Law Documents. In: Camarinha-Matos, L.M., Ortiz, A., Boucher, X., Osório, A.L. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2022. IFIP Advances in Information and Communication Technology, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-031-14844-6_24
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