# Contrastive Enhanced Learning for Multi-Label Text Classification
## 4 Experiments
### Evaluation of Metric Methods  
Our model employs contrastive learning to construct a label space and uses positive label representation alignment to aid in classification. To maintain spatial consistency, we apply the same metric to measure the distance between vectors. However, different distance measurement methods can significantly impact the construction and effectiveness of the label space for prediction. Therefore, we evaluated the model's performance using various distance metrics: cosine distance, Euclidean distance, and Manhattan distance. The results are presented in Figure 2.  
It is evident from Figure 2 that, regardless of the evaluation metric used, the label space constructed using cosine distance consistently outperforms the others, followed by Manhattan distance, with Euclidean distance performing the worst. This indicates that the choice of distance measurement strategy has a global impact on model performance, underscoring the importance of selecting an appropriate metric. We believe cosine distance performs best because it is particularly effective in high-dimensional spaces where the direction of features plays a more critical role than their exact magnitudes. Cosine distance emphasizes the overall similarity of label representations in terms of their direction, which is crucial in multi-label classification, where the relationships between labels often depend on semantic alignment rather than precise numerical differences. Manhattan distance, which calculates the absolute difference between corresponding dimensions and sums these differences across all dimensions, shows robustness due to its ability to treat each dimension independently. This makes it less sensitive to large variations in any single feature, which is advantageous in multi-label classification, where labels or features may vary significantly in importance. However, its overall effectiveness can be limited in handling diverse feature distributions. In contrast, Euclidean distance, which measures the straight-line distance between vectors, is highly sensitive to outliers and variations in individual dimensions. In multi-label classification, where certain dimensions or labels may be noisy or less important, Euclidean distance can disproportionately emphasize these differences. This sensitivity to scale and feature distribution likely explains why  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P@1** & **P@3** & **P@5** & **nDCG@3** & **nDCG@5** \\ \hline SGM & 95.37 & 81.36 & 53.06 & 91.76 & 90.69 \\ DXML & 95.37 & 78.65 & 54.38 & 89.83 & 90.21 \\ HTTN & 95.86 & 78.92 & 55.27 & 89.61 & 90.86 \\ AttentionXML & 96.41 & 80.91 & 56.38 & 91.88 & 92.70 \\ X-Transformer & 96.67 & 82.36 & 57.03 & 93.88 & 94.85 \\ LSAN & 96.81 & 81.89 & 56.92 & 92.83 & 93.43 \\ LightXML & 96.98 & 82.90 & 57.26 & 94.18 & 95.07 \\ \hline
**Ours** & **98.35** & **86.58** & **62.19** & **95.21** & **95.75** \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 2: Results of different metric methods on the AAPD dataset: (**a–c**) represent the best performance of P@1, P@3, and P@5 for different distance metrics.  
Euclidean distance performs the worst in our model, as it struggles to capture the nuanced relationships between labels in the constructed label space.