Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Schöffel, Matthias Arias, Esteban Garces Wiedner, Marinus Ruppert, Paula Li, Meimingwei Heumann, Christian Aßenmacher, Matthias |
| author_facet | Schöffel, Matthias Arias, Esteban Garces Wiedner, Marinus Ruppert, Paula Li, Meimingwei Heumann, Christian Aßenmacher, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages Schöffel, Matthias Arias, Esteban Garces Wiedner, Marinus Ruppert, Paula Li, Meimingwei Heumann, Christian Aßenmacher, Matthias Computation and Language Machine Learning 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. |
| title | Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2506.17715 |