The Automatic Detection of Dataset Names in Scientific Articles

Jenny Heddes, Pim Meerdink, Miguel Pieters and Maarten Marx
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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

Data, 2021, vol. 6, issue 8, 1-19

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.

Keywords: dataset extraction; scientific information extraction; named entity recognition; BERT; SciBERT (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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