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Automatic End-to-End Decomposition and Semantic Annotation of Laws Using High-Performance- Computing and Open Data as a Potential Driver for Digital Transformation

Authors: Alexopoulos, Charalampos; Pihir, Igor; Tomičić Furjan, Martina;

Automatic End-to-End Decomposition and Semantic Annotation of Laws Using High-Performance- Computing and Open Data as a Potential Driver for Digital Transformation

Abstract

This paper is dealing with automatic end-to-end law analysis conducted by decomposition and semantic annotation, by using the high- performance computing and government open data. Legal data and law texts are a category of open data and thus they possess the potential to unlock digital innovation and transformation capacity in governments and businesses, regarding the development of new, better, and more cost-effective services for citizens. For that reason, they can be recognized as a potential digital transformation driver. This research presents a baseline for automation of decomposition and annotation with process and service elements developed for utilization on high-performance computing infrastructure based on government laws open data and gives insights on how the results of it can initiate digital transformation.

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Croatia
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Keywords

Automation, Text mining, Digital Transformation ; Laws Decomposition and Annotation ; Automation ; Text mining ; Hight-Performance-Computing ; Open Data, Open Data, Laws Decomposition and Annotation, Digital Transformation, Hight-Performance-Computing

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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