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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.14688 |
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| _version_ | 1866911274664722432 |
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| author | Gladstone, Clovis Fang, Zhao Stewart, Spencer Dean |
| author_facet | Gladstone, Clovis Fang, Zhao Stewart, Spencer Dean |
| contents | Historical and low-resource NLP remains challenging due to limited annotated data and domain mismatches with modern, web-sourced corpora. This paper outlines our work in using large language models (LLMs) to create ground-truth annotations for historical French (16th-20th centuries) and Chinese (1900-1950) texts. By leveraging LLM-generated ground truth on a subset of our corpus, we were able to fine-tune spaCy to achieve significant gains on period-specific tests for part-of-speech (POS) annotations, lemmatization, and named entity recognition (NER). Our results underscore the importance of domain-specific models and demonstrate that even relatively limited amounts of synthetic data can improve NLP tools for under-resourced corpora in computational humanities research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ground Truth Generation for Multilingual Historical NLP using LLMs Gladstone, Clovis Fang, Zhao Stewart, Spencer Dean Computation and Language Artificial Intelligence Historical and low-resource NLP remains challenging due to limited annotated data and domain mismatches with modern, web-sourced corpora. This paper outlines our work in using large language models (LLMs) to create ground-truth annotations for historical French (16th-20th centuries) and Chinese (1900-1950) texts. By leveraging LLM-generated ground truth on a subset of our corpus, we were able to fine-tune spaCy to achieve significant gains on period-specific tests for part-of-speech (POS) annotations, lemmatization, and named entity recognition (NER). Our results underscore the importance of domain-specific models and demonstrate that even relatively limited amounts of synthetic data can improve NLP tools for under-resourced corpora in computational humanities research. |
| title | Ground Truth Generation for Multilingual Historical NLP using LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2511.14688 |