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Using Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain

: Kirsch, Birgit; Giesselbach, Sven; Schmude, Timothée; Völkening, Malte; Rostalski, Frauke; Rüping, Stefan

Volltext urn:nbn:de:0011-n-6333393 (541 KByte PDF)
MD5 Fingerprint: 4d60874f2b9c3a00028b288cd511d9b6
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Erstellt am: 20.3.2021

Trabold, Daniel (Ed.):
Conference "Lernen, Wissen, Daten, Analysen", LWDA 2020. Proceedings. Online resource : Online, September 9-11, 2020
Online im WWW, 2020 (CEUR Workshop Proceedings 2738)
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) <2020, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Kompetenzzentrum Maschinelles Lernen Rhein-Ruhr ML2R
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
information extraction; Probabilistic Soft Logic; Legal Tech

Extracting information from court process documents to populate a knowledge base produces data valuable to legal faculties, publishers and law firms. A challenge lies in the fact that the relevant information is interdependent and structured by numerous semantic constraints of the legal domain. Ignoring these dependencies leads to inferior solutions. Hence, the objective of this paper is to demonstrate how the extraction pipeline can be improved by the use of probabilistic soft logic rules that reflect both legal and linguistic knowledge. We propose a probabilistic rule model for the overall extraction pipeline, which enables to both map dependencies between local extraction models and to integrate additional domain knowledge in the form of logical constraints. We evaluate the performance of the model on a German court sentences corpus.