딥러닝을 활용한 남북한 의미 변이 탐침 방법론

Abstract

Newspapers ordinarily reflect the meaning of lexical items in two Korean language societies and the changes in vocabulary in the times. This study proposes a methodology that automatically probes for semantic variations in which inter-Korean vocabulary differs from large-scale newspaper data. As a theoretical background, we look at the concepts of the distributional semantics and semantic variations. Next, using deep learning’s word embedding skills, we implement a system to probe semantic variations in an automatic way. This study is significant in the following respects. First, this study systematically concerns the difference in the meaning of inter-Korean vocabulary on a comprehensive scale. Second, this study draws a list of inter-Korean semantic variations by means of the deep learning techniques. Third, this study demonstrates that use of word embedding models facilitate automatic extraction of Korean semantic variations.