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Identifying Implicit Research Data References in Paper Citations

Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026

DOI:10.63317/2g9fq97f2h2j

Abstract

To encourage the public release of research data under open science, it is beneficial to establish mechanisms for evaluating research data based on metrics such as citation counts. In scholarly papers, authors sometimes cite papers that report the creation or release of research data instead of citing the research data themselves. In this paper, as a step toward computing citation counts of research data, we investigate the feasibility of identifying paper citations that refer to research data. We conducted an identification experiment using large language models and evaluated their performance.

Details

Paper ID
lrec2026-ws-nslp-18
Pages
pp. 186-192
BibKey
motegi-etal-2026-identifying
Editors
Georg Rehm, Stefan Dietze, Danilo Dessi, Diana Maynard, Sonja Schimmler
Publisher
European Language Resources Association (ELRA)
ISSN
N/A
ISBN
N/A
Workshop
Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
Location
Palma, Mallorca, Spain
Date
11 - 16 May 2026

Authors

  • KM

    Koshi Motegi

  • KI

    Koichiro Ito

  • SM

    Shigeki Matsubara

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