## 4 Experiments and Results
### Ablation Study on Label Description Components  
To systematically isolate and evaluate the impact of label description embedding in our proposed LDE model, we conducted comprehensive ablation experiments on the ACE 2005 dataset. We designed variations of our model by systematically removing or replacing key components, allowing us to quantify the individual contribution of each element to the overall performance. Table 2 shows the experimental results across different model configurations in few-shot learning scenarios. We evaluated four distinct model variants:  
* Biaffine: Our baseline model without any label embedding components.
* LDE with Trainable Label Features (\(\text{LDE}_{\text{w Label Feature}}\)): Replaces BERT-encoded label information with simple trainable embedding vectors.
* LDE with Label Words Only (\(\text{LDE}_{\text{w Label Word}}\)): Uses only the entity type words (e.g., \"person\", \"organization\") without descriptive context.
* LDE with Full Label Descriptions (\(\text{LDE}_{\text{w Label Description}}\)): Our complete proposed model using detailed semantic descriptions of entity types.  
The experimental results clearly demonstrate the incremental benefits of each component. In 10-shot and 20-shot settings, the \(\text{LDE}_{\text{w Label Feature}}\) model performed similarly to the baseline Biaffine model (49.01% vs. 49.50% F1 in 10-shot, and 58.10% vs. 58.21% F1 in 20-shot), indicating that merely replacing the classification layer with trainable label features provides minimal advantage. However, incorporating semantic information through label words (\(\text{LDE}_{\text{w Label Word}}\)) produced substantial improvements across all few-shot settings compared to both the baseline and trainable feature models. This confirms our hypothesis that leveraging semantic knowledge from the pre-trained BERT model for labels significantly enhances few-shot learning capability. Most notably, the complete \(\text{LDE}_{\text{w Label Description}}\) model, which utilizes comprehensive entity type descriptions, consistently achieved the best performance across all few-shot scenarios, with particularly substantial gains in extremely low-resource settings (five-shot: 44.24% vs. 32.38% for Biaffine, a relative improvement of 36.6%). This systematic progression in performance demonstrates that the rich semantic context provided by full label descriptions is the critical component driving the success of our approach. We observed that the performance gain from label descriptions becomes more pronounced as the training data become scarcer, with the largest relative improvements occurring in the five-shot setting. This finding confirms that incorporating label semantic information is especially valuable in extremely low-resource scenarios, where the model has insufficient examples to learn the conceptual boundaries between entity types. In the one-shot scenario, all models struggle significantly, with even our best model achieving only 5.54% F1. This indicates that while label descriptions provide meaningful improvements, they cannot completely overcome the fundamental challenge of learning from a single example per class. However, as shown in Section IV.D, this limitation can be addressed through transfer learning approaches. These ablation results conclusively demonstrate that the semantic information captured in our label description embeddings is the key factor enabling robust few-shot learning performance, rather than merely architectural changes to the output layer.