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Extracting and quantifying eponyms in full-text articles

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  • Guillaume Cabanac

    (University of Toulouse)

Abstract

Eponyms are known to praise leading scientists for their contributions to science. Some are so widespread that they are even known by laypeople (e.g., Alzheimer’s disease, Darwinism). However, there is no systematic way to discover the distributions of eponyms in scientific domains. Prior work has tackled this issue but has failed to address it completely. Early attempts involved the manual labelling of all eponyms found in a few textbooks of given domains, such as chemistry. Others relied on search engines to probe bibliographic records seeking a single eponym at a time, such as Nash Equilibrium. Nonetheless, we failed to find any attempt of eponym quantification in a large volume of full-text publications. This article introduces a semi-automatic text mining approach to extracting eponyms and quantifying their use in such datasets. Candidate eponyms are matched programmatically by regular expressions, and then validated manually. As a case study, the processing of 821 recent Scientometrics articles reveals a mixture of established and emerging eponyms. The results stress the value of text mining for the rapid extraction and quantification of eponyms that may have substantial implications for research evaluation.

Suggested Citation

  • Guillaume Cabanac, 2014. "Extracting and quantifying eponyms in full-text articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1631-1645, March.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:3:d:10.1007_s11192-013-1091-8
    DOI: 10.1007/s11192-013-1091-8
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    Cited by:

    1. Valderrama-Zurian, J.C. & Melero-Fuentes, D. & Aleixandre-Benavent, R., 2019. "Origin, characteristics, predominance and conceptual networks of eponyms in the bibliometric literature," Journal of Informetrics, Elsevier, vol. 13(1), pages 434-448.
    2. Eugenio Petrovich & Sander Verhaegh & Gregor Bös & Claudia Cristalli & Fons Dewulf & Ties Gemert & Nina IJdens, 2024. "Bibliometrics beyond citations: introducing mention extraction and analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5731-5768, September.
    3. Guillaume Cabanac, 2018. "What is the primordial reference for ...?—Redux," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 481-488, February.
    4. Miguel A. Ortiz Acuña & Adiel T. Almeida Filho & Francisco S. Ramos, 2024. "Modelling the triple helix system innovation of the main economies from Latin America: a coalitional game theory approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3245-3270, June.
    5. Antonio Cavacini, 2015. "Is the CE/BCE notation becoming a standard in scholarly literature?," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1661-1668, February.

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