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Computer Science > Computation and Language

arXiv:2305.04928 (cs)
[Submitted on 5 May 2023]

Title:A transformer-based method for zero and few-shot biomedical
named entity recognition

Authors:Milos Kosprdic, Nikola Prodanovic, Adela Ljajic, Bojana
Basaragin, Nikola Milosevic
Download a PDF of the paper titled A transformer-based method for
zero and few-shot biomedical named entity recognition, by Milo\v{s}
Ko\v{s}prdi\'c and 3 other authors
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    Abstract: Supervised named entity recognition (NER) in the
    biomedical domain is dependent on large sets of annotated texts
    with the given named entities, whose creation can be
    time-consuming and expensive. Furthermore, the extraction of new
    entities often requires conducting additional annotation tasks
    and retraining the model. To address these challenges, this paper
    proposes a transformer-based method for zero- and few-shot NER in
    the biomedical domain. The method is based on transforming the
    task of multi-class token classification into binary token
    classification (token contains the searched entity or does not
    contain the searched entity) and pre-training on a larger amount
    of datasets and biomedical entities, from where the method can
    learn semantic relations between the given and potential classes.
    We have achieved average F1 scores of 35.44% for zero-shot NER,
    50.10% for one-shot NER, 69.94% for 10-shot NER, and 79.51% for
    100-shot NER on 9 diverse evaluated biomedical entities with
    PubMedBERT fine-tuned model. The results demonstrate the
    effectiveness of the proposed method for recognizing new entities
    with limited examples, with comparable or better results from the
    state-of-the-art zero- and few-shot NER methods.

          Collaboration between Bayer Pharma R&D and Serbian
Comments: Institute for Artificial Intelligence Research and
          Development. Submitted to Neural Networks Journal
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2305.04928 [cs.CL]
          (or arXiv:2305.04928v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2305.04928
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Nikola Milosevic Dr [view email]
[v1] Fri, 5 May 2023 12:14:22 UTC (968 KB)
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    Download a PDF of the paper titled A transformer-based method for
    zero and few-shot biomedical named entity recognition, by Milo\v
    {s} Ko\v{s}prdi\'c and 3 other authors
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