Deontic Sentence Classification Using Tree Kernel Classifiers

被引：1

作者：

Liga, Davide ^{[1
,2
]}

Palmirani, Monica ^{[1
]}

机构：

[1] Univ Bologna, Alma Mater Studiorum, Alma Mater Res Inst Human Ctr Artificial Intellig, CIRSFID AI, Bologna, Italy

[2] Univ Luxembourg, Esch Sur Alzette, Luxembourg

来源：

INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 1 | 2023年 / 542卷

关键词：

Deontic; NLP; Legal AI; GDPR; LegalRuleML; Akoma Ntoso; EXTRACTION;

D O I：

10.1007/978-3-031-16072-1_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The aim of this work is to employ Tree Kernel algorithms to classify natural language in the legal domain (i.e. deontic sentences and rules). More precisely, an innovative way of extracting labelled legal data is proposed, which combines the information provided by two famous Lega1XML formats: Akoma Ntoso and LegalRuleML. We then applied this method on the European General Data Protection Regulation (GDPR) to train a Tree Kernel classifier on deontic and non-deontic sentences which were reconstructed using Akoma Ntoso, and labelled using the LegalRuleML representation of the GDPR. To prove the non-triviality of the task we reported the results of a stratified baseline classifier on two classification scenarios.

引用

页码：54 / 73

页数：20

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