SOURCE-LINKED INTELLIGENCE
Multi-Functional Embedding Models for Funder Name Disambiguation in Scientific Publication Records
Understanding the historical allocation and distribution of research funding advances our knowledge of how scientific research is supported across fields, institutions, and regions. However, large-scale analyses are hindered by the lack of comprehensive funder name disambiguation solutions, as funder names often exhibit spelling variations, translations, abbreviations, and inconsistent levels of granularity. In this paper, we present a framework for developing multilingual, multi-functional funder name disambiguation models and demonstrate its application to research publications in biodiversi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-09T10:13:12.000Z
First collected: 2026-09-20T19:32:24.350Z. This is not the publication date.