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

被引：0

作者：

Alexopoulos, Charalampos ^{[1
]}

Pihir, Igor ^{[2
]}

Furjan, Martina Tomicic ^{[2
]}

机构：

[1] Univ Aegean, Dept Informat & Commun Syst Engn, Samos 83200, Greece

[2] Univ Zagreb, Fac Org & Informat, Pavlinska 2, Varazhdin 42000, Croatia

来源：

CENTRAL EUROPEAN CONFERENCE ON INFORMATION AND INTELLIGENT SYSTEMS, CECIIS 2022 | 2022年

关键词：

Digital Transformation; Laws Decomposition and Annotation; Automation; Text mining; Hight-Performance-Computing; Open Data; TEXT-MINING TECHNIQUES; WEB;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：189 / 194

页数：6

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