Primerjava korejskih modelov NER in medjezikovne prilagoditve podatkov za NER v jeziku z malo viri

This study was designed to systematically evaluate and compare the performance of various Korean NER models in the same domain. By doing so, we aim to address the research gap surrounding domain-specific performance variations, which remain under-discussed in existing Korean NER literature. The focus of this research is not merely to report performance metrics but to draw meaningful inferences about the generalizability, robustness, and practical applicability of Korean NER systems in real-world, domain-diverse scenarios. To achieve this, we have selected several state-of-the-art Korean NER models, primarily based on pre-trained transformer architectures. These models were first fine-tuned and validated using a general-purpose Korean NER corpus to establish a consistent baseline. Subsequently, their performance was tested on a domain-specific dataset based on Korean Wikipedia articles. Model evaluation was carried out using standard metrics such as precision, recall, and F1-score. Loss curves were employed to better interpret model training performance. As an additional experiment, we employed a translation process to indirectly address the limitation of Korean NER models in a specific domain. Through this process, we were able to confirm the cross-lingual transferability.