IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v129y2024i11d10.1007_s11192-023-04806-2.html
   My bibliography  Save this article

Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements

Author

Listed:
  • Nina Smirnova

    (GESIS – Leibniz Institute for the Social Sciences)

  • Philipp Mayr

    (GESIS – Leibniz Institute for the Social Sciences)

Abstract

Acknowledgments in scientific papers may give an insight into aspects of the scientific community, such as reward systems, collaboration patterns, and hidden research trends. The aim of the paper is to evaluate the performance of different embedding models for the task of automatic extraction and classification of acknowledged entities from the acknowledgment text in scientific papers. We trained and implemented a named entity recognition (NER) task using the flair NLP framework. The training was conducted using three default Flair NER models with four differently-sized corpora and different versions of the flair NLP framework. The Flair Embeddings model trained on the medium corpus with the latest FLAIR version showed the best accuracy of 0.79. Expanding the size of a training corpus from very small to medium size massively increased the accuracy of all training algorithms, but further expansion of the training corpus did not bring further improvement. Moreover, the performance of the model slightly deteriorated. Our model is able to recognize six entity types: funding agency, grant number, individuals, university, corporation, and miscellaneous. The model works more precisely for some entity types than for others; thus, individuals and grant numbers showed a very good F1-Score over 0.9. Most of the previous works on acknowledgment analysis were limited by the manual evaluation of data and therefore by the amount of processed data. This model can be applied for the comprehensive analysis of acknowledgment texts and may potentially make a great contribution to the field of automated acknowledgment analysis.

Suggested Citation

  • Nina Smirnova & Philipp Mayr, 2024. "Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7261-7285, November.
  • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-023-04806-2
    DOI: 10.1007/s11192-023-04806-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-023-04806-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-023-04806-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-023-04806-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.