Automatic Extraction of Legal Norms: Evaluation of Natural Language Processing Tools

被引：7

作者：

Ferraro, Gabriela ^{[1
,2
]}

Lam, Ho-Pun ^{[1
]}

Tosatto, Silvano Colombo ^{[1
]}

Olivieri, Francesco ^{[1
]}

Islam, Mohammad Badiul ^{[1
]}

van Beest, Nick ^{[1
]}

Governatori, Guido ^{[1
]}

机构：

[1] CSIRO, Data61, Sydney, NSW, Australia

[2] Australian Natl Univ, Canberra, ACT, Australia

来源：

NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE, JSAI-ISAI 2019 | 2020年 / 12331卷

关键词：

Natural Language Processing; Automatic rule extraction; Legal norms; Evaluation; TRANSLATION; SENTENCES;

D O I：

10.1007/978-3-030-58790-1_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Extracting and formalising legal norms from legal documents is a time-consuming and complex procedure. Therefore, the automatic methods that can accelerate this process are in high demand. In this paper, we address two major questions related to this problem: (i) what are the challenges in formalising legal documents into a machine understandable formalism? (ii) to what extent can the data-driven state-of-theart approaches developed in the Natural Language Processing (NLP) community be used to automate the normative mining process. The results of our experiments indicate that NLP technologies such as relation extraction and semantic parsing are promising research avenues to advance research in this area.

引用

页码：64 / 81

页数：18

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