Summary of Advancing Chinese Biomedical Text Mining with Community Challenges, by Hui Zong et al.
Advancing Chinese biomedical text mining with community challenges
by Hui Zong, Rongrong Wu, Jiaxue Cha, Weizhe Feng, Erman Wu, Jiakun Li, Aibin Shao, Liang Tao, Zuofeng Li, Buzhou Tang, Bairong Shen
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study reviews recent advances in community challenges for biomedical text mining in China, focusing on natural language processing tasks such as named entity recognition, entity normalization, and knowledge graph construction. The research analyzed 39 evaluation tasks from six community challenges spanning from 2017 to 2023, exploring potential clinical applications and comparing with English counterparts. The study highlights the contributions, limitations, lessons, and guidelines of these community challenges, emphasizing their role in promoting innovation and fostering collaboration in biomedical text mining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how China is doing in a special kind of computer science called biomedical text mining. They looked at many different tasks that computers have to do with medical texts, like finding important words or making connections between ideas. The researchers found 39 challenges that were done from 2017 to 2023 and saw what kinds of things the computers were good at and not so good at. They compared these challenges to similar ones done in English and talked about how these challenges are helping computer scientists get better at doing important medical work. |
Keywords
* Artificial intelligence * Knowledge graph * Named entity recognition * Natural language processing
