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.