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Nested Named Entity Recognition using Multilayer BERT-based Model: Notebook for the BioASQ Lab at CLEF 2024.

Hasin Rehana1,2, Benu Bansal2,3, Nur Bengisu Çam4

  • 1School of Electrical Engineering & Computer Science, University of North Dakota, Grand Forks, North Dakota, 58202, USA.

CEUR Workshop Proceedings
|April 23, 2026
PubMed
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This summary is machine-generated.

This study presents a novel multilayer BERT model for biomedical named entity recognition (NER), effectively handling complex nested entities. The approach achieved top performance on the BioNNE dataset, improving nested NER accuracy.

Area of Science:

  • Biomedical Natural Language Processing
  • Computational Linguistics
  • Bioinformatics

Background:

  • Named Entity Recognition (NER) is vital in NLP for identifying and categorizing entities in text.
  • Biomedical text presents unique challenges for NER due to complex language structures and nested entities.
  • Existing NER models often struggle with the intricacies of nested and overlapping entities in specialized domains.

Purpose of the Study:

  • To introduce an innovative multilayer BERT-based model for Nested NER in the biomedical domain.
  • To effectively address the complexities of nested entities and overlapping mentions in biomedical literature.
  • To enhance the accuracy and thoroughness of entity detection in specialized scientific texts.

Main Methods:

  • Utilized a multilayer bidirectional encoder representation transformer (BERT) model, specifically pretrained PubMedBERT.
Keywords:
Bidirectional encoder representation transformer (BERT)Named entity recognition (NER)Natural language processing (NLP)Nested NER

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  • Implemented a multilayer tagging process combined with robust contextual embeddings from PubMedBERT.
  • Incorporated UMLS dictionaries to further refine biomedical entity recognition.

    • Main Results:

    • The proposed Multilayer NER Model (MultilayerNERModel) demonstrated superior performance in identifying nested entities.
    • Achieved the highest overall performance in the English-oriented track of the BioASQ competition's BioNNE dataset.
    • Obtained an F1 score of 67.30% and a macro F1 score of 56.36% on the BioNNE English Dataset.

    Conclusions:

    • A multilayer approach significantly enhances the capability of NER models for nested entity recognition in biomedical texts.
    • The MultilayerNERModel effectively manages overlapping entities, improving overall detection accuracy.
    • Combining PubMedBERT embeddings with a multilayer strategy and UMLS dictionaries offers a powerful solution for biomedical NER.