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The Automatic Detection of Dataset Names in Scientific Articles

Author

Listed:
  • Jenny Heddes

    (Informatics Institute, Faculty of Science, University of Amsterdam, Science Park 908, 1098 XH Amsterdam, The Netherlands)

  • Pim Meerdink

    (Informatics Institute, Faculty of Science, University of Amsterdam, Science Park 908, 1098 XH Amsterdam, The Netherlands)

  • Miguel Pieters

    (Informatics Institute, Faculty of Science, University of Amsterdam, Science Park 908, 1098 XH Amsterdam, The Netherlands)

  • Maarten Marx

    (Informatics Institute, Faculty of Science, University of Amsterdam, Science Park 908, 1098 XH Amsterdam, The Netherlands)

Abstract

We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub.

Suggested Citation

  • Jenny Heddes & Pim Meerdink & Miguel Pieters & Maarten Marx, 2021. "The Automatic Detection of Dataset Names in Scientific Articles," Data, MDPI, vol. 6(8), pages 1-19, August.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:8:p:84-:d:608256
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    References listed on IDEAS

    as
    1. Zeng, Tong & Wu, Longfeng & Bratt, Sarah & Acuna, Daniel E., 2020. "Assigning credit to scientific datasets using article citation networks," Journal of Informetrics, Elsevier, vol. 14(2).
    2. Jinseok Kim & Jenna Kim, 2018. "The impact of imbalanced training data on machine learning for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 511-526, October.
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