Advancing Chinese biomedical text mining with community challenges
<|reference_start|>Advancing Chinese biomedical text mining with community challenges: Objective: This study aims to review the recent advances in community challenges for biomedical text mining in China. Methods: We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative analysis were conducted on various biomedical natural language processing tasks, such as named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. Results: We identified 39 evaluation tasks from 6 community challenges that spanned from 2017 to 2023. Our analysis revealed the diverse range of evaluation task types and data sources in biomedical text mining. We explored the potential clinical applications of these community challenge tasks from a translational biomedical informatics perspective. We compared with their English counterparts, and discussed the contributions, limitations, lessons and guidelines of these community challenges, while highlighting future directions in the era of large language models. Conclusion: Community challenge evaluation competitions have played a crucial role in promoting technology innovation and fostering interdisciplinary collaboration in the field of biomedical text mining. These challenges provide valuable platforms for researchers to develop state-of-the-art solutions.<|reference_end|>
@article{zong2024advancing,
    title={Advancing Chinese biomedical text mining with community challenges},
    author={Hui Zong, Rongrong Wu, Jiaxue Cha, Weizhe Feng, Erman Wu, Jiakun Li,
  Aibin Shao, Liang Tao, Zuofeng Li, Buzhou Tang, Bairong Shen},
    journal={Journal of Biomedical Informatics. 2024;157:104716.},
    year={2024},
    doi={10.1016/j.jbi.2024.104716},
    archivePrefix={arXiv},
    eprint={2403.04261},
    primaryClass={cs.AI cs.CL cs.LG}
}