Abstract
Part-of-speech (POS) tagging remains a foundational component in natural language processing pipelines, particularly critical for historical text analysis at the intersection of computational linguistics and digital humanities. Despite significant advancements in modern large language models (LLMs) for ancient languages, their application to Medieval Romance languages presents distinctive challenges stemming from diachronic linguistic evolution, spelling variations, and labeled data scarcity. This study systematically investigates the central determinants of POS tagging performance across diverse corpora of Medieval Occitan, Medieval Spanish, and Medieval French texts, spanning biblical, hagiographical, medical, and dietary domains. Through rigorous experimentation, we evaluate how fine-tuning approaches, prompt engineering, model architectures, decoding strategies, and cross-lingual transfer learning techniques affect tagging accuracy. Our results reveal both notable limitations in LLMs' ability to process historical language variations and non-standardized spelling, as well as promising specialized techniques that effectively address the unique challenges presented by low-resource historical languages.
Abstract (translated)
词性标注（POS）仍然是自然语言处理管道中的一个基础组成部分，特别是在计算语言学和数字人文科学交汇处的历史文本分析中尤为重要。尽管现代大型语言模型（LLM）在古代语言应用上取得了显著进展，但这些模型在处理中世纪罗曼语系语言时面临着独特的挑战，这些问题源自历时性的语言演变、拼写变化以及标注数据的稀缺性。本研究系统地调查了词性标注性能在不同中世纪奥克语、西班牙语和法语文本语料库中的关键决定因素，这些文本涵盖了圣经、圣徒传记、医学和饮食等领域。通过严格的实验，我们评估了微调方法、提示工程、模型架构、解码策略以及跨语言迁移学习技术如何影响词性标注的准确性。我们的研究结果揭示了LLM处理历史语言变化及非标准化拼写时存在的显著局限，并且还发现了一些有前景的专业技术，这些技术能够有效应对低资源历史语言所面临的独特挑战。
URL
https://arxiv.org/abs/2506.17715
