Research Entity Extraction and Topic Detection from UKRI Grant Proposals
This paper presents preliminary findings from a UKRI-funded Metascience project comparing three LLM-based approaches, GPT-4o, Mistral, and a bespoke algorithm, DSIT-Taxonomies, for extracting and classifying research entities from funding proposals. Our project "Tracking Stars and Unicorns" aims to identify early signals of emerging research areas to inform public investment. Our methodology employed a three-stage pipeline, leveraging Mistral for primary entity extraction and mapping against the OpenAlex Topics taxonomy. We evaluated our approach across 42 proposals' abstracts from different areas and observed that Mistral and GPT-4o produce comparable, high-quality entity sets with signi...
| Authors | Xingran Ruan, Angelo Salatino, Rosa Filgueira et al. |
|---|---|
| Primary category | cs.DL |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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