Researchers have developed a novel workflow to transform historical show programs into structured, interoperable data. This process combines vision-language models (VLMs) for accurate text transcription with a custom extension of the Linked Art ontology for the performing arts. The method was tested using Festival d'Avignon programs from 1947 to the present, achieving over 98% transcription accuracy and significantly reducing semantic annotation hallucinations by constraining large language model generation through a knowledge graph. This approach promises to enable large-scale, interoperable historiography for the performing arts, facilitating the creation of catalogues raisonnés for artists. AI
IMPACT Enables large-scale digital archiving and analysis of performing arts history.
RANK_REASON The item is an academic paper detailing a new methodology for data processing. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- arXivLabs
- Bernard Jacquemin
- Biblioth uevoque nationale de France
- CatalyzeX
- DagsHub
- Festival d'Avignon
- Gotit.pub
- Hugging Face
- Influence Flower
- large language model
- Linked Art
- ScienceCast
- vision-language model
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