# Conditional Knowledge Extraction Using Contextual Information Enhancement
## 5 Experiment
### Results Analysis
#### 5.2.4 Case Study  
This paper presents a case study that validates the weighting of attention assigned by the proposed method. Figure 9 illustrates the attention score of our framework on a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Name** & **Models** & **Acc** & **F1** & **Rec** & **P** \\ \hline cpd-en & \(BERT_{+}\)_attention_ & **81.25\%** & **80.78\%** & **79.27\%** & **82.35\%** \\  & \(BERT_{+}\)_attention_ + _BiLSTM_ & 76.04\% & 73.31\% & 73.17\% & 73.45\% \\ \hline cpd-en & \(CBERT_{+}\)_attention_ & 80.21\% & 76.88\% & 76.83\% & 76.94\% \\  & \(CBERT_{+}\)_attention_ + _BiLSTM_ & **82.29\%** & **79.48\%** & **79.27\%** & **79.69\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ablation experiments with the BERT model on the cpd-en dataset, and results of ablation experiments with the CBERT model on the cpd-en dataset. The bolded data indicates the best row of results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Name** & **Models** & **Acc** & **F1** & **Rec** & **P** \\ \hline cpd-cn & \(WoBERT_{+}\)_Phrase granularity_ & 83.04\% & 81.18\% & 81.19\% & 81.18\% \\  & \(WoBERT_{+}\)_Phrase and entity granularity_ & **86.61\%** & **86.65\%** & **86.14\%** & **87.17\%** \\ \hline cpd-en & \(BERT_{+}\)_Phrase granularity_ & 78.13\% & 76.74\% & 76.83\% & 76.67\% \\  & \(BERT_{+}\)_Phrase and entity granularity_ & **82.29\%** & **79.47\%** & **79.27\%** & **79.69\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The WoBERT model was fine-tuned on the cpd-cn dataset using phrase and entity granularity, while the BERT model was fine-tuned on the cpd-en dataset using both phrase and entity granularity. The bolded data indicates the best row of results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Name** & **Models** & **Acc** & **F1** & **Rec** & **P** \\ \hline  & \(WoBERT_{+}\)_Attention_ & 78.57\% & 79.40\% & 79.21\% & 79.60\% \\ cpd-cn & \(WoBERT_{+}\)_attention_ & **85.71\%** & **84.16\%** & **84.16\%** & **84.16\%** \\  & \(WoBERT_{+}\)_attention_ + _BiLSTM_ & 83.93\% & 83.21\% & 83.17\% & 83.24\% \\ \hline cpd-cn & \(CBERT_{+}\)_attention_ & 82.14\% & 81.18\% & 81.19\% & 81.18\% \\  & \(CBERT_{+}\)_attention_ + _BiLSTM_ & **86.61\%** & **86.65\%** & **86.14\%** & **87.17\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation experiments with the WoBERT model on the cpd-cn dataset, and results of ablation experiments with the CBERT model on the cpd-cn dataset. The bolded data indicates the best row of results.  
random sentence: \"fishing vessels should $ travel at speeds $ greater than # 18 km/h # & under normal conditions & and travel at speeds less than 22 km/h under emergencies.\" After incorporating syntactic analysis of dependency and sentence context information, the experimental results demonstrate that high attention scores are primarily distributed among words with dependency relations. Adding markers makes some vocabulary clearer, even though not all are apparent. The implication of the attention score is to allow the model to adjust the weight of the semantic features at each moment in the final feature vector of the sentence according to the attention weights, which can highlight the role of words with high relevance in the matching results. From the results, it can be inferred that the model can focus on words with specific dependency relations in the classification process.