TY - GEN
T1 - Eliciting Semantic Types of Legal Norms in Korean Legislation with Deep Learning
AU - Lam, Ho Pun
AU - Phan, Thi Thuy
AU - Hashmi, Mustafa
AU - The, Kiet Hoang
AU - Lo, Sin Kit
AU - Choi, Yongsun
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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 features, we propose a taxonomy of semantic type in korean legislation, such as obligations, rights, permissions, penalties, etc. Based on this, we performed automatic classification of legal norms with a rule-based classifier using a manually labelled dataset formed by three korean acts, i.e., Insurance Business Act, Banking Act and Financial Holding Companies Act, of the Korean legislation (n= 1237 ) and a performance of F1= 0.97 was reached. In contrast, several supervised machine learning based classifiers were implemented and a performance of F-measure = 0.99 was achieved.
AB - 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 features, we propose a taxonomy of semantic type in korean legislation, such as obligations, rights, permissions, penalties, etc. Based on this, we performed automatic classification of legal norms with a rule-based classifier using a manually labelled dataset formed by three korean acts, i.e., Insurance Business Act, Banking Act and Financial Holding Companies Act, of the Korean legislation (n= 1237 ) and a performance of F1= 0.97 was reached. In contrast, several supervised machine learning based classifiers were implemented and a performance of F-measure = 0.99 was achieved.
KW - Korean legislation
KW - Legal norms classification
KW - Legal taxonomy
KW - Natural language processing
KW - Semantic types
UR - http://www.scopus.com/inward/record.url?scp=85138804514&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-14602-2_4
DO - 10.1007/978-3-031-14602-2_4
M3 - Conference Proceeding
AN - SCOPUS:85138804514
SN - 9783031146015
T3 - Communications in Computer and Information Science
SP - 70
EP - 93
BT - Knowledge Discovery, Knowledge Engineering and Knowledge Management - 12th International Joint Conference, IC3K 2020, Revised Selected Papers
A2 - Fred, Ana
A2 - Fred, Ana
A2 - Aveiro, David
A2 - Aveiro, David
A2 - Dietz, Jan
A2 - Salgado, Ana
A2 - Bernardino, Jorge
A2 - Filipe, Joaquim
A2 - Filipe, Joaquim
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2020
Y2 - 2 November 2020 through 4 November 2020
ER -