# A New Model for Emotion-Driven Behavior Extraction from Text
## 6 Experiments
### Ablation Study  
In this section, we detail the results of an ablation study to analyze the importance of different components in generating \(P\) as defined in Equation (9). The components we examined include the document \(D\), the emotion \(s_{e}\), the behavior \(s_{b}\), and the template \(\tau\). Each component plays a unique role in the feature learning process, and we aim to assess their individual contributions to the overall performance of our model.  
To achieve this, we performed ablation experiments at both the clause and span levels. We removed \(D\) and \(\tau\) from \(P\) and replaced \(s_{e}\) with the placeholder \"a certain emotion\". Additionally, we replaced \(s_{b}\) with the clause \(c_{b}\) in which \(s_{b}\) occurs. We evaluated the performance of the model using precision, recall, and \(F_{1}\) score metrics, and the results are presented in Table 7.  
The results of our ablation study demonstrate the significant impact of each component on the feature learning process and validate the effectiveness of all four components. In particular, removing any component leads to a notable decrease in performance. For example, when \(D\) is removed from the prompt, the precision, recall, and \(F_{1}\) score at the clause level drop by 21.35%, 10.19%, and 16.69%, respectively, and the span-level metrics decrease by 18.00%, 11.74%, and 14.13%, respectively. Similarly, removing \(\tau\) results in decreases of 2.20%, 1.74%, and 1.85%, respectively, in the clause-level metrics, as well as a  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Emotion Extraction**} & \multicolumn{3}{c}{**Behavior Extraction**} & \multicolumn{3}{c}{**E–B Pair Extraction**} \\ \hline
**Model** & \(P\) **(\%)** & \(R\) **(\%)** & \(F_{1}\) **(\%)** & \(P\) **(\%)** & \(R\) **(\%)** & \(F_{1}\) **(\%)** & \(P\) **(\%)** & \(R\) **(\%)** & \(F_{1}\) **(\%)** \\ \hline PL-Marker [31] & 95.59 & **97.74** & **96.65** & **81.89** & 54.15 & 65.19 & 71.05 & 47.01 & 56.59 \\ PURE [32] & 95.31 & 96.74 & 96.02 & 78.92 & 55.51 & 65.18 & 66.20 & 47.26 & 55.15 \\ UniRE [30] & 94.12 & 96.73 & 95.40 & 77.81 & 81.31 & 79.52 & 70.11 & 76.06 & 72.97 \\ \hline
**Ours** & **95.81** & 97.49 & **96.65** & 79.24 & **85.71** & **82.35** & **72.09** & **77.11** & **74.52** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of existing relation extraction approaches and our approach on the EDBE task at the span level.  
decrease of 2.11%, 1.74%, and 1.90%, respectively, in the span-level metrics. These findings underscore the crucial role of both \(D\) and \(\tau\) in the feature learning process.  
Furthermore, we observed a notable performance difference between the original template and the modified one wherein \(s_{b}\) is replaced by its corresponding clause, \(c_{b}\). Specifically, the precision is relatively high, while the recall is comparatively low. This indicates that including a clause as a behavior in the template adds additional information and makes the model more stringent during training, thereby impacting its overall performance. These results suggest that our span-level dataset for the EDBE task has an advantage over clause-level datasets such as ECE and ECE, as EDBE can capture more nuanced information at the span level, leading to improved performance [23; 38; 39].  
In summary, our ablation study provides valuable insights into the impact of different components on the feature learning process. The results highlight the significance of all four components and demonstrate that removing any component significantly affects the overall performance of the model.