Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages
It addresses challenges in historical text analysis for computational linguistics and digital humanities, but appears incremental as it builds on existing methods for known bottlenecks.
This study tackled the problem of part-of-speech tagging for low-resource Medieval Romance languages by evaluating factors like fine-tuning and cross-lingual transfer, revealing limitations in large language models and promising specialized techniques.
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.