[ADAPT] AI-Assisted Pattern Detection for the Analysis of Medieval Poetic Translations
Ente: EC
Scadenza: 2029-08-14
Importo max: 267.419 EUR
Paese: EU
Descrizione
Despite the rapid advances and accessibility of digital technologies, computational research remains strikingly underdeveloped in the study of early Germanic cultural heritage – especially poetry. This is not due to a lack of interest or material, but to structural obstacles: annotated corpora are scarce, and building them is both time-consuming and undervalued in a field where publication pressure is high. On top of this, the technical complexity of digital methods – which involves, for example, learning Python-programming or XML-TEI markup – deters many scholars, discouraging the integration of computational approaches into mainstream philological research.
ADAPT addresses this bottleneck by harnessing AI technology – in particular large language models (LLMs). At their core, LLMs excel at identifying patterns, a capacity that mirrors a central task in philological inquiry: tracing correspondences and recurring features. On this basis, ADAPT will develop the first replicable workflow for AI-assisted textual analysis on pre-modern sources, drastically lowering the technical threshold for computational analysis and enabling scalable, rigorous exploration of historical texts without requiring advanced programming skills.
This workflow will then be tested on the only poetic translations from Latin into Old Norse to investigate how medieval translators retextualised Latin sources within the Norse poetic system. These unique case studies offer an exceptional opportunity to trace strategies of literary adaptation and cultural negotiation across traditions.
Hosted at the University of Bergen, with secondments at Ljubljana (AI4DH) and UC Berkeley, ADAPT combines philology, translation studies, and digital humanities. The project will promote the responsible integration of AI in the humanities, generating methodological innovation and insights into medieval translation practices, and providing a transferable model for future research on historical textual traditions.
Settori: Horizon Europe Topics
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