## 4 Experiments and Results
### Additional Analysis  
Additionally, to confirm that the proposed model is robust in nested named entity recognition tasks, we examined the performance by entity type in one five-shot case during ACE 2005 nested NER experiments. Table 4 includes results from [9], who explored nested NER using GPT-4 through in-context learning. Their methodology utilized GPT-4 in a five-shot learning setting. In their approach, the authors found that decomposed-QA techniques, which were effective for flat NER, showed decreased performance when applied to nested NER. To address this limitation, they introduced specialized inference techniques designed specifically for nested entity structures and incorporated detailed label information in their prompts. Their experiments on the ACE 2005 dataset followed the same evaluation criteria (flat, nested, and nesting) that we use in this study, allowing for direct comparison with our proposed methods. Table 4 shows the performance by entity type for one five-shot case in the ACE 2005 dataset. Performance evaluation utilized the nested type-specific named entity recognition performance from [23]. Here,'standard' is the basic method that evaluates performance for each named entity, 'flat' evaluates only flat named entities, 'nested' evaluates only nested named entities without judging nested relationships, and 'nesting' is an evaluation method that assesses whether all entity tags inside and outside are correct, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ACE 2005 (Nested)**} \\ \hline  & **Source Domain** & **1-Shot** & **5-Shot** & **10-Shot** & **20-Shot** \\ \hline LDE & - & 5.54 \(\pm\) 3.27 & 44.24 \(\pm\) 2.01 & 57.19 \(\pm\) 1.27 & 64.50 \(\pm\) 1.02 \\ LDE & GENIA & 14.28 \(\pm\) 7.26 & 50.94 \(\pm\) 1.59 & 59.92 \(\pm\) 1.96 & 66.99 \(\pm\) 0.75 \\ LDE & CoNLL 2003 English & 33.22 \(\pm\) 7.33 & 58.06 \(\pm\) 0.57 & 62.44 \(\pm\) 1.21 & 68.13 \(\pm\) 0.80 \\ \hline \multicolumn{4}{c}{GENIA (Nested)} \\ \hline  & Source Domain & 1-Shot & 5-Shot & 10-Shot & 20-Shot \\ \hline LDE & - & 11.60 \(\pm\) 2.51 & 45.07 \(\pm\) 3.57 & 47.90 \(\pm\) 2.27 & 61.46 \(\pm\) 1.62 \\ LDE & CoNLL 2003 English & 35.14 \(\pm\) 0.72 & 49.98 \(\pm\) 1.42 & 57.51 \(\pm\) 0.63 & 64.75 \(\pm\) 0.56 \\ LDE & ACE 2005 & 31.67 \(\pm\) 1.95 & 48.52 \(\pm\) 1.69 & 58.88 \(\pm\) 1.56 & 65.35 \(\pm\) 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Transfer learning effects on ACE 2005 and GENIA dataset performance. including nested relationships. The BERT-LSTM-CRF model, despite using the nested tag approach from [6], completely failed to handle named entity recognition considering nested relationships in the five-shot environment. The biaffine model and \(\text{LDE}_{\text{w}}\) Label Feature model can handle nested named entity recognition but showed lower performance than the BERT-LSTM-CRF model. Models using BERT-embedded vectors for labels, \(\text{LDE}_{\text{w}}\) Label Word and \(\text{LDE}_{\text{w}}\) Label Description, both showed high performance; the \(\text{LDE}_{\text{w}}\) Label Description model proposed in this paper demonstrated high performance with nested entities, indicating its strength in nested named entity recognition in few-shot environments. Notably, we have included results from [9], who explored using GPT-4 for nested NER through in-context learning approaches. As shown in Table 4, GPT-4, despite being one of the most advanced and largest language models with billions of parameters, achieves 34.75%, 38.29%, and 6.63% F1 scores on flat, nested, and nesting evaluations, respectively, in a five-shot setting. Our \(\text{LDE}_{\text{w}}\) Label Description model, which is based on a much smaller BERT architecture, achieves competitive or superior performance with 54.48%, 28.31%, and 2.62% on the same metrics.