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NAISTSOC at the NTCIR-16 Real-MedNLP Task | Synapse

April 1, 2026Open Access

NAISTSOC at the NTCIR-16 Real-MedNLP Task

Puntos clave

The aim is to address the challenges of Medical Natural Language Processing in the Real-MedNLP task.
Participated in NTCIR-16 Real-MedNLP task
Utilized BERT model for natural language processing
Trained the BERT model on specific medical data
Achieved the best results in the task
Reported an optimal F1-score from the trained model

Resumen

This paper describes how we tackled the Medical Natural Language Processing for Real-MedNLP task as participants of NTCIR16. We utilized BERT model for solving this task. We found that BERT model we trained is the best results with F1-score.

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Cite This Study

Nishiyama et al. (Tue,) studied this question.

synapsesocial.com/papers/69cd7b345652765b073a9010 https://doi.org/https://doi.org/10.20736/0002002301

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1FRDC at the NTCIR-16 Real-MedNLP Task2022
2AMI Team at the NTCIR-16 Real-MedNLP Task2022
3NICTmed at the NCTIR-16 Real-MedNLP Task2022
4SRCB at the NTCIR-16 Real-MedNLP Task2022
5Syapse at the NTCIR-16 RealMed-NLP task2022