Paper Title:Computational linguistic processing for evaluating policy effectiveness: textual analysis of China-Korea continuing education regulations
Authors: Tongle Li
Corresponding Author: Tongle Li (hglx88@163.com ), South Korea
Abstract
This study employs computational linguistic methods to compare continuing education regulatory frameworks in Korea and China via systematic text comparison. Applying algorithmic methods like thematic decomposition, sentiment analysis, and semantic correlation measures to the government reports of the two countries, we develop an innovative cross-cultural assessment framework. The analytical process integrates entity extraction, vector-based semantic mapping, and quantitative content mining in order to identify regulatory patterns and efficacy signals within policy documents from 2010 to 2023. Empirical results show notable divergence in administrative priorities, discursive frameworks, and governance styles, with Chinese regulations showing centralized coordination characteristics in contrast to Korea’s market-responsive institutions. The research adds to the policy analysis literature by demonstrating computational methodologies’ ability to identify obscured administrative priorities and operational nuances outside of conventional analytical grasp. The contribution enhances computational policy studies through the creation of replicable, unbiased processes for comparative cross-country regulation, inferring useful implications for administrators and researchers constructing streamlined continuing education models. The research confirms computational linguistics as an effective means of evidence-based policy analysis in multilingual settings.
Keywords
Computational linguistics, Policy analysis, Lifelong learning,Cross-national comparison,Sino-Korean relations,Content mining
