In this paper, we propose a novel way of improving named entity recognition (NER) in the Korean language using its language-specific features. While the field of NER has been studied extensively in recent years, the mechanism of efficiently recognizing named entities (NEs) in Korean has hardly been explored. This is because the Korean language has distinct linguistic properties that present challenges for modeling. Therefore, an annotation scheme for Korean corpora by adopting the CoNLL-U format, which decomposes Korean words into morphemes and reduces the ambiguity of NEs in the original segmentation that may contain functional morphemes such as postpositions and particles, is proposed herein. We investigate how the NE tags are best represented in this morpheme-based scheme and implement an algorithm to convert word-based and syllable-based Korean corpora with NEs into the proposed morpheme-based format. Analyses of the results of traditional and neural models reveal that the proposed morpheme-based format is feasible, and the varied performances of the models under the influence of various additional language-specific features are demonstrated. Extrinsic conditions were also considered to observe the variance of the performances of the proposed models, given different types of data, including the original segmentation and different types of tagging formats.