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Two SEBIS Paper were nominated for LexisNexis Best Paper Award

Feb 25, 2018

Two papers of SEBIS were nominated for the LexisNexis Best Paper Award at IRIS 2018.

The first paper is about Named Entity Recognition, Extraction, and Linking in German Legal Documents and was written by Ingo Glaser, Benrhard Waltl and Florian Matthes. Please look here for furhter information about this paper.

The second paper from Daniel Braun, Elena Scepankova, Patrick Holl, and Florian Matthes is titled Customer-Centered LegalTech: Automated Analysis of Standard Form Contracts.

  legal informatics iris best paper publication legaltech

Paper on automated extraction of semantic information from german legal documents is nominated for LexisNexis Best Paper Award

Feb 22, 2017

Accepted for LexisNexis Best Paper Award

 

Abstract

Based on a collaborative data science environment, and a large document corpus (> 130.000 documents from German tax law) we demonstrate the extraction of semantic information. This paper shows the potential of rule-based text analysis to automatically extract semantic information, such as the year of dispute in cases. Additionally, it demonstrates the extraction of legal definitions in laws and the usage of terms in a defining context. Based on an iterative and interdisciplinary process, involving legal experts, software engineers, and data scientists, to evaluate and continuously refine the model used for the computer-supported extraction.
  legal informatics iris publication legaltech

Paper on A Data Science Environment for Semantic Analysis of German Legal Texts accepted

Jan 29, 2016

A paper has been accepted for publication at the Internationels Symposium on Legal Informatics (IRIS). In the paper entitled LEXIA - A Data Science Environment for Semantic Analysis of German Legal Texts, the author team consisting of Bernhard Waltl, Florian Matthes, Tobias Waltl, and Thomas Grass show how modern technologies of text mining algorithms can be applied to the legal texts. It shows how to consider the particularities of legal texts, such as laws or judgments, in the data model and provides a reference architecture for a collaborative data science environment.

  legal informatics lexalyze 2016 iris