Adapting PromptORE for Modern History: Information Extraction from Hispanic Monarchy Documents of the XVIth Century
H\`ector Loopez Hidalgo, Michel Boeglin, David Kahn, Josiane Mothe,, Diego Ortiz, David Panzoli

TL;DR
This paper adapts PromptORE for extracting relations from 16th-century Spanish Inquisition documents by fine-tuning transformer models and engineering prompts, significantly improving accuracy over standard methods.
Contribution
It introduces a biasing technique through fine-tuning and prompt engineering to enhance relation extraction in historical Spanish texts, addressing entity placement and gender issues.
Findings
Up to 50% accuracy improvement with Biased PromptORE
Effective handling of complex entity placements and gender issues
Validated by expert assessments and benchmark evaluations
Abstract
Semantic relations among entities are a widely accepted method for relation extraction. PromptORE (Prompt-based Open Relation Extraction) was designed to improve relation extraction with Large Language Models on generalistic documents. However, it is less effective when applied to historical documents, in languages other than English. In this study, we introduce an adaptation of PromptORE to extract relations from specialized documents, namely digital transcripts of trials from the Spanish Inquisition. Our approach involves fine-tuning transformer models with their pretraining objective on the data they will perform inference. We refer to this process as "biasing". Our Biased PromptORE addresses complex entity placements and genderism that occur in Spanish texts. We solve these issues by prompt engineering. We evaluate our method using Encoder-like models, corroborating our findings…
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Taxonomy
TopicsNatural Language Processing Techniques
