## 4 Experiments and Results
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**GENIA (Nested)**} \\ \hline  & **1-Shot** & **5-Shot** & **10-Shot** & **20-Shot** & **100\%** \\ \hline FewNER [13] & 23.24 \(\pm\) 0.73 & 29.19 \(\pm\) 0.64 & & & \\ FIT [7] & & 34.43 \(\pm\) 9.06 & 44.98 \(\pm\) 3.38 & 51.26 \(\pm\) 3.96 & \\ LPNER [8] & 26.32 \(\pm\) 3.88 & 44.99 \(\pm\) 2.20 & & & \\ BERT-LSTM-CRF (our) & 17.71 \(\pm\) 8.33 & 17.91 \(\pm\) 8.39 & 29.16 \(\pm\) 1.79 & 42.12 \(\pm\) 8.46 & 76.82 \\ Biaffine (our) & 5.75 \(\pm\) 2.58 & 30.74 \(\pm\) 2.95 & 31.93 \(\pm\) 1.93 & 50.65 \(\pm\) 2.39 & 78.20 \\ LDE (our) & 11.60 \(\pm\) 2.51 & 45.07 \(\pm\) 3.57 & 47.90 \(\pm\) 2.27 & 61.46 \(\pm\) 1.62 & 79.01 \\ \hline  & \multicolumn{4}{c}{ACE 2004 (Nested)} \\ \hline  & 1-Shot & 5-Shot & 10-Shot & 20-Shot & 100\% \\ \hline FIT [7] & & 35.87 \(\pm\) 4.92 & 44.88 \(\pm\) 4.82 & 53.92 \(\pm\) 2.99 & \\ LPNER [8] & 25.67 \(\pm\) 7.05 & 42.67 \(\pm\) 7.55 & & & \\ BERT-LSTM-CRF (our) & 10.84 \(\pm\) 7.17 & 18.94 \(\pm\) 9.28 & 34.19 \(\pm\) 4.30 & 49.70 \(\pm\) 2.96 & 82.38 \\ Biaffine (our) & 3.52 \(\pm\) 1.95 & 28.10 \(\pm\) 1.91 & 47.25 \(\pm\) 1.10 & 55.98 \(\pm\) 0.88 & 85.77 \\ LDE (our) & 4.06 \(\pm\) 3.04 & 42.23 \(\pm\) 2.30 & 57.04 \(\pm\) 1.19 & 62.86 \(\pm\) 0.56 & 85.82 \\ \hline  & \multicolumn{4}{c}{ACE 2005 (Nested)} \\ \hline  & 1-Shot & 5-Shot & 10-Shot & 20-Shot & 100\% \\ \hline FIT [7] & & 37.74 \(\pm\) 5.33 & 42.25 \(\pm\) 10.65 & 52.71 \(\pm\) 2.55 & \\ LPNER [8] & 25.01 \(\pm\) 10.83 & 46.62 \(\pm\) 5.82 & & & \\ BERT-LSTM-CRF (our) & 5.74 \(\pm\) 6.49 & 30.80 \(\pm\) 5.89 & 41.46 \(\pm\) 1.49 & 51.88 \(\pm\) 1.24 & 80.94 \\ Biaffine (our) & 3.17 \(\pm\) 1.84 & 32.38 \(\pm\) 2.96 & 49.50 \(\pm\) 2.34 & 58.21 \(\pm\) 0.97 & 83.95 \\ LDE (our) & 5.54 \(\pm\) 3.27 & 44.24 \(\pm\) 2.01 & 57.19 \(\pm\) 1.27 & 64.50 \(\pm\) 1.02 & 84.23 \\ \hline  & \multicolumn{4}{c}{CoNLL 2003 English (Flat)} \\ \hline  & 1-Shot & 5-Shot & 10-Shot & 20-Shot & 100\% \\ \hline SpanNER [14] & & 71.1 \(\pm\) 0.4 & & & \\ BERT-LSTM-CRF (our) & 26.55 \(\pm\) 8.55 & 46.64 \(\pm\) 1.67 & 49.74 \(\pm\) 3.82 & 63.61 \(\pm\) 1.85 & 89.86 \\ Biaffine (our) & 9.22 \(\pm\) 9.30 & 36.95 \(\pm\) 4.00 & 23.15 \(\pm\) 6.85 & 50.82 \(\pm\) 2.56 & 91.81 \\ LDE (our) & 6.61 \(\pm\) 7.50 & 42.53 \(\pm\) 7.04 & 45.05 \(\pm\) 2.38 & 63.85 \(\pm\) 1.85 & 92.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of NER models across different datasets. existing research and the BERT-LSTM-CRF model, indicating that the relatively complex LDE model is difficult to train adequately in one-shot environments. This limitation in extreme few-shot scenarios can be attributed to several factors. First, the architectural complexity of the LDE model requires more training examples to properly optimize its parameters compared to simpler models like BERT-LSTM-CRF. Unlike generative large language models that rely heavily on pre-training, our approach depends on learning the relationships between label descriptions and input spans within a specialized architecture. The high standard deviations observed in one-shot experiments (e.g., 5.54 \(\pm\) 3.27% for ACE 2005) further suggest that performance is highly sensitive to the specific examples chosen for training, creating potential training instability. As demonstrated in Section IV.D, this limitation can be effectively addressed through transfer learning from related domains, which provides the model with a more robust foundation even when target domain examples are extremely limited. In experiments with CoNLL 2003 English, a flat named entity recognition dataset, there was a minimal performance difference between BERT-LSTM-CRF, biaffine, and LDE models when trained on the full dataset, indicating that this is a relatively easier dataset compared to other nested NER datasets (GENIA, ACE 2004, ACE 2005).