## 4 Experiments and Results
### Additional Analysis
This comparison demonstrates that our proposed approach can achieve comparable or even better performance on nested entity recognition tasks using significantly fewer parameters and computational resources compared to large language models like GPT-4. To provide deeper insights into how label descriptions influence entity span classification, we conducted a qualitative analysis of specific cases from the ACE 2005 dataset. Table 5 presents four representative examples comparing the predictions of different model variants against gold standard annotations. These cases highlight the enhanced capabilities of our label description approach in handling nested entity recognition challenges. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ACE 2005 (Nested) 5-Shot** & & & \\ \hline \hline \multirow{2}{*}{In-context Learning (GPT-4) [9]} & & 34.75 & 38.29 & 6.63 \\  & BERT-LSTM-CRF & 33.85 & 36.56 & 25.74 & 0.00 \\  & Biaffine & 31.65 & 40.48 & 17.11 & 0.33 \\  & 27.31 & 37.02 & 15.11 & 0.54 \\  & 39.92 & 47.59 & 27.69 & 3.51 \\  & 43.98 & 54.48 & 28.31 & 2.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Entity type-specific performance analysis in 5-shot ACE 2005. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Case 1**} \\ \hline \hline \multicolumn{2}{c}{**Model**} & **Result** \\ \hline \hline \multirow{2}{*}{Sentence} & [CLS] We have been so damned busy with the holidays (that’s what we call December at our house) \\  & that I just haven’t had time. [SEP] \\ \hline \multirow{2}{*}{Gold} & (1, 1, ’We’, ’PER’) (15, 15, ’we’, ’PER’) (19, 19, ’our’, ’PER’) (19, 20, ’our house’, ’FAC’) \\  & (23, 23, ’T’, ’PER’) \\ \hline \multirow{2}{*}{Biaffine} & (1, 1, ’We’, ’ORG’) **(15, 15, ’we’, ’PER’)** (15, 23, ’we call December at our house) that I’, ’PER’) \\  & **(23, 23, ’T’, ’PER’)** \\ \hline \multirow{2}{*}{\(\text{LDE}_{\text{w}}\) Label Feature} & **(1, 1, ’We’, ’PER’)** (1, 15, ’We have been so damned busy with the holidays (that ’s what we”, ’PER’) \\  & (1, 23, “We have been so damned busy with the holidays (that ’s what we call December at our house) that I’, ’PER’) \\  & house) that I’, ’PER’) **(15, 15, ’we’, ’PER’)** (15, 23, ’we call December at our house) that I’, ’PER’) \\  & **(23, 23, ’T’, ’PER’)** \\ \hline \multirow{2}{*}{\(\text{LDE}_{\text{w}}\) Label Word} & (1, 1, ’We’, ’ORG’) **(15, 15, ’we’, ’PER’)** (19, 19, ’our’, ’GPE’) **(23, 23, ’T’, ’PER’)** \\  & (1, 1, ’We’, ’ORG’) **(15, 15, ’we’, ’PER’)** (19, 19, ’our’, ’GPE’) **(23, 23, ’T’, ’PER’)** \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sample cases showing entity recognition results across different model variants for ACE 2005 dataset. ## symbols indicate token separation marks due to BERT’s WordPiece Tokenizer. As shown in Table 5 and Figure 2, the LDE model with label descriptions (\(\text{LDE}_{\text{w Label Description}}\)) consistently captures more complex entity relationships compared to other variants. In Case 1, while all models correctly identify some of the person entities, only the \(\text{LDE}_{\text{w Label Description}}\) model recognizes the pronoun \"our\" as a potential entity, although it misclassifies it as GPE rather than PER. This suggests that the label descriptions provide richer semantic context that helps the model identify less obvious entity mentions. Case 2 demonstrates the model's ability to recognize nested facility and location entities. The sentence contains a complex nested structure where \"the lane to the farm\" is a facility with \"the farm\" nested within it. The baseline Biaffine model only identifies person entities and misses this nested structure entirely. In contrast, the \(\text{LDE}_{\text{w Label Description}}\) model correctly identifies both the outer facility entity and the inner location entity, showing how semantic information in label descriptions helps distinguish between closely related entity types in hierarchical structures.