## 4 Experiments and Results
### Transfer Learning  
Named entity recognition faces the challenge that tags differ by domain, making it difficult for simple classification-based NER models to utilize transfer learning with training data from different domains. However, the proposed LDE model can apply transfer learning to any NER domain as it learns label descriptions through the BERT model.  
Table 3 shows comparison experiments of transfer learning by source domain data on both the ACE 2005 and GENIA datasets. The experimental results show that in few-shot experiments, all performances improved compared to the basic LDE model, demonstrating the potential of models using transfer learning. When attempting transfer learning after training on CoNLL 2003 English data, which is a flat NER dataset from a similar domain and is most easily obtainable, the model achieved a performance of 33.22 in one-shot scenarios on ACE 2005. This shows that transfer learning can overcome the disadvantage of poor performance in extreme few-shot environments, such as one-shot, which was a limitation of the LDE model proposed in this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ACE 2005 (Nested)**} \\ \hline \multicolumn{1}{c}{**Avg. Tokens Per Label Description**} & \multicolumn{1}{c}{**1-Shot**} & \multicolumn{1}{c}{**5-Shot**} & \multicolumn{1}{c}{**10-Shot**} & \multicolumn{1}{c}{**20-Shot**} \\ \hline Biaffine & & 3.17 \(\pm\) 1.84 & 32.38 \(\pm\) 2.96 & 49.50 \(\pm\) 2.34 & 58.21 \(\pm\) 0.97 \\ \(\text{LDE}_{\text{w Label Feature}}\) & & 0.00 & 15.71 \(\pm\) 10.01 & 49.01 \(\pm\) 1.76 & 58.10 \(\pm\) 0.63 \\ \(\text{LDE}_{\text{w Label Word}}\) & 4.0 & 1.96 \(\pm\) 1.62 & 37.75 \(\pm\) 3.28 & 54.27 \(\pm\) 2.27 & 63.42 \(\pm\) 1.09 \\ \(\text{LDE}_{\text{w Label Description}}\) & 43.13 & 5.54 \(\pm\) 3.27 & 44.24 \(\pm\) 2.01 & 57.19 \(\pm\) 1.27 & 64.50 \(\pm\) 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact of different label description types on ACE 2005 performance.  
Similarly, for the GENIA dataset, we conducted transfer learning experiments using models trained on CoNLL 2003 English and ACE 2005 as source domains. These results highlight an important characteristic of our approach: while the LDE model leverages label descriptions, it requires minimal examples from the target domain to adapt effectively. This is expected, as our model fundamentally depends on fine-tuning with domain-specific examples to learn the relationships between label descriptions and domain contexts.  
The challenges in cross-domain adaptation can be primarily attributed to the non-overlapping entity tag sets between different domains. For instance, the biomedical domain (GENIA) uses highly specialized entity types like G#DNA, G#Protein, and G#RNA that have no direct correspondence in general domain datasets like CoNLL 2003 or ACE 2005.  
However, it is worth noting that with even minimal target domain examples (one-shot), transfer learning significantly improves performance compared to training from scratch. For instance, when transferring from CoNLL 2003 to GENIA, the one-shot performance jumps to 35.14% (compared to 11.60% without transfer), and five-shot performance reaches 49.98% (compared to 45.07% without transfer). Similar improvements are observed when transferring from ACE 2005 to GENIA. This demonstrates that the model can rapidly adapt with minimal target domain data, making this approach viable for real-world scenarios where obtaining a few labeled examples is typically feasible.