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arXiv Computation and Language By Kanyao Han, Zhiwen You, Jinseok Kim, Jana Diesner

Multi-Functional Embedding Models for Funder Name Disambiguation in Scientific Publication Records

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The paper introduces a multilingual, multi-functional framework for disambiguating funder names in scientific publications, using a training dataset that merges the Research Organization Registry with Web of Science and Crossref Open Funder Registry data. By applying multi-task learning with contrastive and multiple negatives ranking losses, the authors fine‑tune open‑weight embedding models from the Sentence Transformer, Gemma, and Qwen3 families, achieving over 90% accuracy in matching Web of Science funder names to ROR identifiers and surpassing general‑purpose LLMs by more than 0.1. For funders not present in ROR, a similarity network is constructed to identify clusters, and the study discusses challenges related to smaller and non‑English‑speaking funders.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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