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.

中文翻译：

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基于语言特定特征的韩语命名实体识别

在本文中，我们提出了一种利用韩语特定语言特征来改进韩语命名实体识别（NER）的新方法。尽管近年来 NER 领域得到了广泛的研究，但有效识别韩语命名实体（NE）的机制却很少被探索。这是因为韩语具有独特的语言特性，这给建模带来了挑战。因此，本文提出了一种采用CoNLL-U格式的韩语语料标注方案，将韩语单词分解为语素，并减少原始分词中可能包含后置词和助词等功能语素的NE的歧义性。我们研究了如何在这种基于语素的方案中最好地表示 NE 标签，并实现一种算法，将带有 NE 的基于单词和基于音节的韩语语料库转换为所提出的基于语素的格式。对传统模型和神经模型结果的分析表明，所提出的基于语素的格式是可行的，并且证明了模型在各种附加语言特定特征的影响下的不同性能。考虑到不同类型的数据（包括原始分割和不同类型的标记格式），还考虑了外部条件来观察所提出模型的性能差异。并演示了在各种附加语言特定功能的影响下模型的不同性能。考虑到不同类型的数据（包括原始分割和不同类型的标记格式），还考虑了外部条件来观察所提出模型的性能差异。并演示了在各种附加语言特定功能的影响下模型的不同性能。考虑到不同类型的数据（包括原始分割和不同类型的标记格式），还考虑了外部条件来观察所提出模型的性能差异。