Publication List

  1. Shimizu, S., Wakamiya, S., and Aramaki, E. (2026).
    A Herd of Language Models Makes a Better Zero-shot Annotator for Clinical Named Entity Recognition.
    Findings of ACL 2026.
    [Paper]
  2. Shimizu, S. et al. (2026).
    J-ClinicalBench: A Benchmark for Evaluating Large Language Models on Practical Clinical Tasks in Japanese.
    LREC 2026.
    [Paper]
  3. Shimizu, S., Hisada, S., Uno, Y., Yada, S., Wakamiya, S., and Aramaki, E. (2025).
    Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-Sharing Restrictions.
    Findings of ACL 2025, pp.14678–14694.
    [Paper]
  4. Shimizu, S., Baroud, I., Raithel, L., Yada, S., Wakamiya, S., and Aramaki, E. (2025).
    RecordTwin: Towards Creating Safe Synthetic Clinical Corpora.
    Findings of ACL 2025, pp.14714–14726.
    [Paper]
  5. Shimizu, S., Nishiyama, T., Nagai, H., Wakamiya, S., and Aramaki, E. (2025).
    Toward Cross-Hospital Deployment of NLP Systems: Model Development and Validation of Fine-Tuned Large Language Models for Disease Name Recognition in Japanese.
    JMIR Medical Informatics, 13(1): e76773.
    [Paper]
  6. Shimizu, S., Yada, S., Wakamiya, S., and Aramaki, E. (2024).
    Generating Distributable Surrogate Corpus for Medical Multi-Label Classification.
    CL4Health @ LREC-COLING, pp.153–162.
    [Paper]
  7. Shimizu, S., Pereira, L., Yada, S., and Aramaki, E. (2024).
    QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation.
    LREC-COLING 2024, pp.13371–13381.
    [Paper]
  8. Shimizu, S., Yada, S., Raithel, L., and Aramaki, E. (2024).
    Improving Self-Training with Prototypical Learning for Source-Free Domain Adaptation on Clinical Text.
    BioNLP 2024, pp.1–13.
    [Paper]