A named entity recognition and classification plays the first and foremost
important role in capturing semantics in data and anchoring in translation as
well as downstream study for history. However, NER in historical text has faced
challenges such as scarcity of annotated corpus, multilanguage variety, various
noise, and different convention far different from the contemporary language
model. This paper introduces Korean historical corpus (Diary of Royal secretary
which is named SeungJeongWon) recorded over several centuries and recently
added with named entity information as well as phrase markers which historians
carefully annotated. We fined-tuned the language model on history corpus,
conducted extensive comparative experiments using our language model and
pretrained muti-language models. We set up the hypothesis of combination of
time and annotation information and tested it based on statistical t test. Our
finding shows that phrase markers clearly improve the performance of NER model
in predicting unseen entity in documents written far different time period. It
also shows that each of phrase marker and corpus-specific trained model does
not improve the performance. We discuss the future research directions and
practical strategies to decipher the history document.

该研究介绍了朝鲜历史文献中的命名实体识别和分类，通过将语言模型调整到历史文献上，与使用预训练的多语言模型进行对比实验，发现短语标记明显改善了 NER 模型在预测不同时间段撰写的未见过的实体时的性能。

穿越数个世纪的迁移学习：机器和历史学家集成方法解密王室秘书日记

Transfer Learning across Several Centuries: Machine and Historian  Integrated Method to Decipher Royal Secretary's Diary

Large language models (LLMs) have been leveraged for several years now,
obtaining state-of-the-art performance in recognizing entities from modern
documents. For the last few months, the conversational agent ChatGPT has
"prompted" a lot of interest in the scientific community and public due to its
capacity of generating plausible-sounding answers. In this paper, we explore
this ability by probing it in the named entity recognition and classification
(NERC) task in primary sources (e.g., historical newspapers and classical
commentaries) in a zero-shot manner and by comparing it with state-of-the-art
LM-based systems. Our findings indicate several shortcomings in identifying
entities in historical text that range from the consistency of entity
annotation guidelines, entity complexity, and code-switching, to the
specificity of prompting. Moreover, as expected, the inaccessibility of
historical archives to the public (and thus on the Internet) also impacts its
performance.

本文探讨大型语言模型在历史文本中的命名实体识别和分类任务中的应用，通过比较其与基于最先进的语言模型的系统的表现找出其存在的问题。研究结果表明，历史文本中的命名实体识别存在多个问题，包括实体注释规范的一致性、实体复杂性和代码切换等，这些问题导致大型语言模型与其他系统相比存在着一些缺点。