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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.22345 |
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| _version_ | 1866908561240489984 |
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| author | Spliethoff, Sophie Hoeken, Sanne Schwandt, Silke Zarrieß, Sina Alaçam, Özge |
| author_facet | Spliethoff, Sophie Hoeken, Sanne Schwandt, Silke Zarrieß, Sina Alaçam, Özge |
| contents | In this paper, we aim at the application of Natural Language Processing (NLP) techniques to historical research endeavors, particularly addressing the study of religious invectives in the context of the Protestant Reformation in Tudor England. We outline a workflow spanning from raw data, through pre-processing and data selection, to an iterative annotation process. As a result, we introduce the InviTE corpus -- a corpus of almost 2000 Early Modern English (EModE) sentences, which are enriched with expert annotations regarding invective language throughout 16th-century England. Subsequently, we assess and compare the performance of fine-tuned BERT-based models and zero-shot prompted instruction-tuned large language models (LLMs), which highlights the superiority of models pre-trained on historical data and fine-tuned to invective detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22345 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling Spliethoff, Sophie Hoeken, Sanne Schwandt, Silke Zarrieß, Sina Alaçam, Özge Computation and Language In this paper, we aim at the application of Natural Language Processing (NLP) techniques to historical research endeavors, particularly addressing the study of religious invectives in the context of the Protestant Reformation in Tudor England. We outline a workflow spanning from raw data, through pre-processing and data selection, to an iterative annotation process. As a result, we introduce the InviTE corpus -- a corpus of almost 2000 Early Modern English (EModE) sentences, which are enriched with expert annotations regarding invective language throughout 16th-century England. Subsequently, we assess and compare the performance of fine-tuned BERT-based models and zero-shot prompted instruction-tuned large language models (LLMs), which highlights the superiority of models pre-trained on historical data and fine-tuned to invective detection. |
| title | The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.22345 |