# A New Model for Emotion-Driven Behavior Extraction from Text
## 6 Experiments
### Evaluation at the Span Level  
In this section, we present a comparison of our approach with several potent relation extraction approaches. The results are presented in Table 6. At the span level, our model predicts emotion spans or behavior spans that should align with the annotated emotion spans or behavior spans, making this task similar to conventional relation extraction.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Emotion Extraction**} & \multicolumn{3}{c}{**Behavior Extraction**} & \multicolumn{3}{c}{**E–B \({}^{*}\) Pair Extraction**} \\ \hline
**Model** & \(P\) **(\%)** & **R (\%)** & **F (\%)** & **P (\%)** & **R (\%)** & **F (\%)** & **P (\%)** & **R (\%)** & **F (\%)** \\ \hline ECPE [23] & 84.67 & 78.95 & 81.71 & 84.43 & 75.78 & 79.87 & 79.59 & 79.80 & 79.39 \\ ECPE-2D [44] & 94.60 & 89.22 & 91.83 & 85.68 & 79.32 & 82.38 & 84.95 & 84.52 & 84.73 \\ ECPE-MLL [45] & 92.96 & 84.96 & 88.78 & 84.94 & 77.41 & 81.00 & 83.60 & 80.20 & 81.87 \\ CD-MRC [46] & 96.15 & 25.06 & 39.76 & 84.73 & 15.10 & 25.63 & **85.71** & 29.01 & 43.35 \\ UTOS [28] & 84.36 & 86.51 & 85.42 & 84.41 & 89.57 & 86.91 & 81.08 & 83.97 & 82.50 \\ \hline
**Ours** & **98.47** & **98.98** & **98.73** & **91.72** & **96.01** & **93.82** & 83.26 & **89.05** & **86.05** \\ \hline \hline \end{tabular} \({}^{*}\)E–B is the abbreviation of emotion–behavior.  
\end{table}
Table 5: Results of existing ECPE approaches and our approach on the EDBE task at the clause level.  
Table 6 shows the results of existing relation extraction approaches and our approach for the EDBE task at the span level. The results of our method are highlighted in bold. We compared our method with three state-of-the-art relation extraction approaches, including PL-Marker [31], PURE [32], and UniRE [30].  
In the span-level evaluation, our approach achieves competitive results compared to existing relation extraction approaches. It outperforms PL-Marker and PURE in emotion extraction and behavior extraction tasks in terms of precision, recall, and the \(F_{1}\) score. Our approach also achieves the highest \(F_{1}\) score in the E-B pair extraction task.  
Regarding the emotion extraction sub-task, our method did not demonstrate a significant improvement. This can be attributed to the task's similarity to standard entity extraction tasks, where the extracted emotion spans tend to be small in size. Consequently, achieving substantial advancements in a well-established task such as emotion extraction is challenging.  
Conversely, in the behavior extraction sub-task, we observed a noticeable improvement of 2.83% in the \(F_{1}\) score. This improvement can be attributed to the longer length of behavior spans and the dispersed nature of their semantic information, which make this sub-task more challenging to address.  
In the E-B (emotion-behavior) pair extraction sub-task, we also achieved a considerable improvement. This improvement is attributed to the utilization of prompts, which provided additional semantic information to the model, aiding in achieving better results.  
Overall, our method shows significant improvements over existing approaches, both at the clause and span levels. The ability to capture fine-grain information and leverage semantic and contextual cues enables our model to achieve better performance in emotion, behavior, and E-B pair extraction tasks.