Authors:
Thi Thuy Thuy Phan
1
;
Ho-Pun Lam
2
;
Mustafa Hashmi
2
;
3
and
Yongsun Choi
4
Affiliations:
1
CMC Ciber, Vietnam
;
2
Data61, CSIRO, Sydney, Australia
;
3
La Trobe Law School, La Trobe University, Melbourne, Australia
;
4
Inje University, Gimhae, Republic of Korea
Keyword(s):
Korean Legislation, Legal Taxonomy, Legal Norms Classification, Semantic Types.
Abstract:
Automating information extraction from legal documents and formalising them into a machine understandable format has long been an integral challenge to legal reasoning. Most approaches in the past consist of highly complex solutions that use annotated syntactic structures and grammar to distil rules. The current research trend is to utilise state-of-the-art natural language processing (NLP) approaches to automate these tasks, with minimum human interference. In this paper, based on its functional aspects, we propose a legal taxonomy of semantic types in Korean legislation, such as definitional provision, deeming provision, penalty, obligation, permission, prohibition, etc. In addition to this, a NLP classifier has been developed to facilitate the automated legal norms classification process and an overall F1 score of 0.97 has been achieved.