# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 7 Conclusions  
This review comprehensively analyzes flat and nested NER tasks in the biomedical domain, underscoring recent advancements and persisting challenges. While LLMs have markedly improved the performance of flat NER tasks, substantial challenges remain to address the complexities of nested NER. Our analysis highlighted the limitations of nested entity processing strategies that require excessive resources and utilize span representation methods that lack contextual considerations. Additionally, the limited availability of annotated datasets containing diverse entity types is a significant bottleneck for scaling NER tasks. Furthermore, annotation inconsistencies across different corpora hinder models from achieving reliable performance on datasets of similar types. Future research efforts should focus on developing robust models that can maintain consistent performance across different datasets and achieve high-quality learning with limited resources. Addressing these challenges is essential for building reliable BioNER systems that can be effectively used in real-world biomedical applications.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14209302/s1](https://www.mdpi.com/article/10.3390/app14209302/s1)]([https://www.mdpi.com/article/10.3390/app14209302/s1](https://www.mdpi.com/article/10.3390/app14209302/s1)), Table S1: Performance evaluation of traditional approaches and LLM-based models on the flat NER datasets; Table S2: Examples of the type discrepancy among biomedical datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Decoding** & \multicolumn{3}{c}{**NCBI Disease**} & \multicolumn{3}{c}{**BC2 GM**} & \multicolumn{3}{c}{**BC5 CDR Chemical**} \\
**Layer** & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline SoftMax & 86.14 & 89.10 & 87.60 & 82.85 & 84.08 & 83.46 & 92.36 & 93.46 & 92.90 \\ \hline CRF & 86.04 & 89.27 & 87.62 & 83.49 & **84.77** & 84.13 & 92.74 & 93.68 & 93.20 \\ \hline RNN & **87.32** & **89.40** & **88.35** & 83.61 & 84.45 & 84.02 & 92.49 & **93.96** & **93.22** \\ \hline Span labeling & 87.06 & 87.48 & 87.27 & **85.36** & 83.31 & **84.32** & **93.38** & 92.43 & 92.91 \\ \hline \hline \end{tabular}
**Bold** indicates the best scores, and underline indicates second-best scores.  
\end{table}
Table 6: Comparison of the flat NER performance according to the decoding layer.  
Author Contributions:Conceptualization, Y.P., G.S. and M.R.; investigation, Y.P. and G.S.; writing--original draft preparation, Y.P. and G.S.; writing--review and editing, Y.P. and M.R.; supervision, M.R.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (20220517) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2020-II201373, Artificial Intelligence Graduate School Program (Hanyang University)).  
The authors declare no conflict of interest.