  In the paper, we propose a novel way of improving named entity recognition in
the Korean language using its language-specific features. While the field of
named entity recognition has been studied extensively in recent years, the
mechanism of efficiently recognizing named entities in Korean has hardly been
explored. This is because the Korean language has distinct linguistic
properties that prevent models from achieving their best performances.
Therefore, an annotation scheme for {Korean corpora} by adopting the CoNLL-U
format, which decomposes Korean words into morphemes and reduces the ambiguity
of named entities in the original segmentation that may contain functional
morphemes such as postpositions and particles, is proposed herein. We
investigate how the named entity tags are best represented in this
morpheme-based scheme and implement an algorithm to convert word-based {and
syllable-based Korean corpora} with named entities into the proposed
morpheme-based format. Analyses of the results of {statistical 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.
