The field of biomedical research has witnessed a significant increase in the
accumulation of vast amounts of textual data from various sources such as
scientific literatures, electronic health records, clinical trial reports, and
social media. However, manually processing and analyzing these extensive and
complex resources is time-consuming and inefficient. To address this challenge,
biomedical text mining, also known as biomedical natural language processing,
has garnered great attention. Community challenge evaluation competitions have
played an important role in promoting technology innovation and
interdisciplinary collaboration in biomedical text mining research. These
challenges provide platforms for researchers to develop state-of-the-art
solutions for data mining and information processing in biomedical research. In
this article, we review the recent advances in community challenges specific to
Chinese biomedical text mining. Firstly, we collect the information of these
evaluation tasks, such as data sources and task types. Secondly, we conduct
systematic summary and comparative analysis, including 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. Then, we summarize the potential clinical applications of these
community challenge tasks from translational informatics perspective. Finally,
we discuss the contributions and limitations of these community challenges,
while highlighting future directions in the era of large language models.

对中文生物医学文本挖掘的社区挑战进行了综述和比较分析，总结了其在生物医学研究中的潜在临床应用以及在大语言模型时代的贡献、局限和未来发展方向。

推进生物医学文本挖掘的社区挑战

Advancing Biomedical Text Mining with Community Challenges

Recent work demonstrated that flow-based invertible neural networks are
promising tools for solving ambiguous inverse problems. Following up on this,
we investigate how ten invertible architectures and related models fare on two
intuitive, low-dimensional benchmark problems, obtaining the best results with
coupling layers and simple autoencoders. We hope that our initial efforts
inspire other researchers to evaluate their invertible architectures in the
same setting and put forth additional benchmarks, so our evaluation may
eventually grow into an official community challenge.

研究探讨了基于流的可逆神经网络在逆问题求解方面较为有效，并进一步结合十种可逆体系结构和相关模型解决低维基准问题，发现耦合层和简单自编码器表现最佳。希望该研究能促进其他研究者在相似情境中评估他们的可逆体系结构，并提出其他基准问题，以便该评估最终发展成为官方社区挑战。