Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.04261 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910583406723072 |
|---|---|
| author | Zong, Hui Wu, Rongrong Cha, Jiaxue Feng, Weizhe Wu, Erman Li, Jiakun Shao, Aibin Tao, Liang Li, Zuofeng Tang, Buzhou Shen, Bairong |
| author_facet | Zong, Hui Wu, Rongrong Cha, Jiaxue Feng, Weizhe Wu, Erman Li, Jiakun Shao, Aibin Tao, Liang Li, Zuofeng Tang, Buzhou Shen, Bairong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04261 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Chinese biomedical text mining with community challenges Zong, Hui Wu, Rongrong Cha, Jiaxue Feng, Weizhe Wu, Erman Li, Jiakun Shao, Aibin Tao, Liang Li, Zuofeng Tang, Buzhou Shen, Bairong Artificial Intelligence Computation and Language Machine Learning 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. |
| title | Advancing Chinese biomedical text mining with community challenges |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2403.04261 |