# Chinese Event Detection without Triggers Based on Dual Attention
## 4 Experiment
### Experiment Results Analysis  
#### 4.2.1 Performance Comparison Analysis  
To demonstrate that EDWTDA can improve event detection precision and recall, EDWTDA was compared with the seven baselines mentioned in Section 4.1.2, and the results are shown in Table 6. The bold part represents the optimal experimental results. The task of event detection is split into two parts: trigger word recognition and trigger word classification. TBNNAM and EDWTDA are trigger-free event detection methods, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Trigger Recognition**} & \multicolumn{4}{c}{**Trigger Classification**} \\ \cline{2-7}  & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F1}\) & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F1}\) \\ \hline DMCNN [3] & 66.60 & 63.60 & 65.07 & 61.60 & 58.80 & 60.20 \\ HNN [15] & 74.20 & 63.10 & 68.20 & 77.10 & 53.10 & 63.00 \\ \hline HBTNGMA [26] & 54.29 & 62.82 & 58.25 & 49.86 & 57.69 & 53.49 \\ \hline NPN [9] & 70.63 & 64.74 & 67.56 & 67.13 & 61.54 & 64.21 \\ TLNN [10] & 67.34 & 74.68 & 70.82 & 64.45 & 71.47 & 67.78 \\ JMCEE [37] & **84.30** & **80.40** & **82.30** & 76.40 & 71.70 & 73.97 \\ TBNNAM [11] & / & / & / & 76.20 & 64.50 & 69.86 \\
**EDWTDA** & / & / & / & **79.80** & **75.60** & **77.64** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of experimental results of different event detection methods. (a) ACE 2005 dataset. (b) Dam safety operation log dataset. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Value** \\ \hline Word Embedding Size & 312 \\ Entity Type Embedding Size & 200 \\ Lexical Annotation Embedding Size & 200 \\ BiLSTM Hidden Size & 256 \\ Local Attention Network Hidden Size & 128 \\ Global Attention Network Hidden Size & 128 \\ Dropout Rate & 0.5 \\ Batch Size & 16 \\ Epoch Size & 100 \\ Learning Rate & 0.002 \\ \(\lambda\) & 0.44 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyper-parameter settings. which can determine the event type directly, so only the latter half of the experimental results are available. In Table 6, EDWTDA outperforms other baselines on both datasets, achieving the best precision, recall, and F1-score. According to the experimental results on the ACE 2005 dataset, EDWTDA improved the accuracy rate, recall rate, and F1-score by 3.40%, 3.90%, and 3.67%, respectively, compared with the optimal baseline JMCEE. And the experimental results on the dam safety operation log dataset prove that EDWTDA improves the accuracy rate, recall rate, and F1-score by 4.18%, 4.58%, and 4.39%, respectively, compared with the optimal baseline JMCEE. The overall effect of the dam safety operation log dataset is better than the ACE 2005 dataset. Because the dam event types are limited and the sentence structure has relatively fixed formats. Compared to public datasets, the meanings are relatively simple and unambiguous, making it easier to determine the event type. Uneven data distribution is an obvious long-tail problem of the ACE 2005 Chinese dataset, as is shown in Figure 4. The results demonstrate that EDWTDA performs well on data-poor event types. The experimental results also show that DMCNN, as a classical event detection model, not only outperforms HBTNGMA in terms of precision, recall, and F1-Score on both datasets but also has a significant gap with the other one. HNN has the highest precision on the ACE 2005 dataset. HNN combines CNN and BiLSTM, which can better capture context and improve the model's accuracy. However, it has the lowest recall. HBTNGMA is highlighted in gray in the experimental results because it had no pre-trained model during the experiments. HBTNGMA uses documents to detect multiple events in a sentence. And its precision and recall are both improved over DMCNN. However, it does not solve the problem of mismatch caused by word separation in Chinese text, and there is little room for improvement.