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Smart Contracts and Blockchain - a huge hype or an actual disruptive technology

On Thursday, January 24th, 2019, at 6:00 pm, the Seitz Weckbach Fackler & Partner law firm hosted an event organized by the Augsburg Law Society on Smart Contracts and Blockchain - a Huge hype or an actual disruptive technology? At the lecture evening, Prof Dr. Hans Grigoleit and Prof. Dr. Florian Matthes gave a talk on the diverse technical and legal implications of smart contracts and blockchain from the perspective of computer science on the one hand, and the lawyers perspective on the other.

The presentation slides of Prof. Dr. Florian Matthes talk are available here for download.


Paper on Classifying Semantic Types of Legal Sentences - Portability of Machine Learning Models has been accepted at Jurix 2018

The team of Florian Matthes, consisting of Ingo Glaser, and Elena Scepankova, has published their recent results on Classifying Semantic Types of Legal Sentences: Portability of Machine Learning Models at the 31st international conference on legal knowledge and information systems (Jurix). The paper is going to be presented at the conference from 12-14 December 2018, Netherlands, Groningen.

More details can be found here.

 


Paper titled Towards Explainable Semantic Text Matching accepted at JURIX 2018

The growing amount of textual data in the legal domain leads to a demand for better text analysis tools adapted to legal domain specific use cases. Semantic Text Matching (STM) is the general problem of linking text fragments of one or more document types. The STM problem is present in many legal document analysis tasks, such as argumentation mining. A common solution approach to the STM problem is to use text similarity measures to identify matching text fragments. In this paper, we recapitulate the STM problem and a use case in German tenancy law, where we match tenancy contract clauses and legal comment chapters. We propose an approach similar to local interpretable model-agnostic explanations (LIME) to better understand the behavior of text similarity measures like TFIDF and word embeddings. We call this approach eXplainable Semantic Text Matching (XSTM).


Two SEBIS Paper were nominated for LexisNexis Best Paper Award

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.


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

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.