## 5 Results and Analyses
### Experimental Analyses
#### 5.2.3 Case Study  
This section first compares the case prediction outcomes of the CLEAR model with the previous best REIC model. Subsequently, attention matrix heatmaps of the CLEAR model are generated and analyzed. (1) Comparative Analysis of Prediction Results  
Table 7 present a comparison of relation prediction results between the CLEAR and REIC models on case studies. It shows that the REIC model failed to adequately distinguish \begin{table}
\begin{tabular}{l c c} \hline \hline
**Attention Method** & **F1** & **AUC** \\ \hline Uniform Attention & 64.12 & 65.43 \\ Graph Attention (GAT) & 66.12 & 67.21 \\ Metric Learning Attention & 66.62 & 67.98 \\ Multi-Head Attention & 65.89 & 66.87 \\
**PDWA (ours)** & **68.42** & **68.78** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Attention mechanism comparison on CodRED. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **F1** & **AUC** & **P@500** & **P@1000** \\ \hline CLEAR (our model) & 67.29 & 69.02 & 82.21 & 66.69 \\ -IC & 65.83 & 67.46 & 80.75 & 64.98 \\ -BR & 64.18 & 65.92 & 79.37 & 63.52 \\ -CP & 65.20 & 66.85 & 80.14 & 64.23 \\ -TL & 66.54 & 68.15 & 81.43 & 65.87 \\ -UA & 65.62 & 66.98 & 80.42 & 64.78 \\ -RF & 64.89 & 66.24 & 79.88 & 64.05 \\ -DW & 64.33 & 66.17 & 79.65 & 63.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation studies on the CodRED development set with RoBERTa encoder. semantic nuances due to the high semantic similarity between document paths containing target relational facts (paths 1-4) and those without (paths 5-10). Specifically, BERT encoding in REIC struggled with fine-grained semantic differentiation, while its policy network's reward function lacked contrastive incentives for path discrimination, resulting in ineffective attention allocation and erroneous predictions. The CLEAR model addresses these limitations by leveraging its PDWA module to achieve fine-grained adjustments of attention weights. For path 2, CLEAR identifies the term \"purchased\" as most relevant to the \"owned by\" relation. For paths 3 and 4, it highlights \"monitoring\" as critical to the \"maintained by\" relation. This capability enables CLEAR to effectively differentiate between paths containing target relational representations (paths 1-4) and irrelevant ones (paths 5-10). Furthermore, by analyzing distinctions in relational descriptors (e.g., \"purchased\" vs. \"monitoring\"), CLEAR successfully disentangles the coexisting \"owned by\" and \"maintained by\" relational facts between the target entities. (2) Analysis of Attention Matrix Heatmaps  
To further verify the model's effective learning capability for cross-document path relation dependencies, this study conducted a case analysis based on the target entity pair (_Utah State Route_ 209, _UDOT_) from the CodRED dataset. By visualizing the normalized attention matrix heatmap for the target relation unit (\(h_{1},t_{1}\)) (Figure 3), we reveal the model's decision-making mechanism. This heatmap presents the distribution of attention weights across different entity relations in matrix form, where row coordinate \(t(i)\) represents the tail entity of the \(i\)-th path (e.g., _UDOT_), and column coordinate \(b(i)(j)\) corresponds to the \(j\)-th bridging entity in the \(i\)-th path (e.g., _1_-15 highway).