# Tibetan Judicial Event Extraction Based on Deep Word Representation and Hybrid Neural Networks
## 4 Experiments
### Main Results  
To evaluate the effectiveness of the proposed model for Tibetan judicial event extraction, this study conducts a comparative analysis. Several different event extraction models were selected, including CNN-rand, LSTM-rand, CNN-pre-train, LSTM-pre-train, Bi-LSTM, and CINO. The details of these models and their corresponding parameter settings are provided in Table 5.  
For the above benchmark models using Tibetan pre-trained syllable vectors, the 768-dimensional CINO syllable vectors were used. To be consistent with the CINO model, the dimension was set to 768 when randomly initializing the Tibetan syllable vectors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Hyperparameters** & **Value** \\ \hline
1 & batch\_size & 4 \\
2 & learning rate & \(6\times 10^{-5}\) \\
3 & kernel\_size & \(3\times 3\) \\
4 & num\_filters & 32 \\
5 & max\_seq\_len & 500 \\
6 & optimizer & AdamW \\
7 & hidden\_size & 128 \\
8 & dropout\_rate & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter settings. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Abbr** & **Event Type** & **Args** & **Syllables** &
\begin{tabular}{c} **Sent\_len** \\ **(avg/max/min)** \\ \end{tabular} & **Quantity** \\ \hline
1 & TF & \(\text{Theft/}_{\pi\text{avg}}\) & 6 & 71,459 & 160.58/349/41 & 445 \\
2 & CO & \(\text{Concal}\frac{\text{Concal}}{\text{Concal}}\) & 3 & 18,969 & 83.93/348/17 & 226 \\
3 & DF & \(\text{Defraud/}_{\pi\text{avg}}\) & 5 & 3780 & 72.69/155/36 & 52 \\
4 & AR & \(\text{Arrest/}_{\pi\text{avg}}\) & 4 & 51,762 & 58.69/290/7 & 882 \\
5 & AP & \(\text{Appraise/}_{\pi\text{avg}}\) & 3 & 26,165 & 87.22/203/20 & 300 \\
6 & RS & \(\text{Resell/}_{\pi\text{avg}}\) & 4 & 38,579 & 108.37/329/21 & 356 \\
7 & BU & \(\text{Buy/}_{\pi\text{avg}}\) & 4 & 5579 & 84.53/169/31 & 66 \\
8 & RB & \(\text{Robbery/}_{\pi\text{avg}}\) & 7 & 9384 & 86.89/363/37 & 108 \\
9 & DT & \(\text{Death/}_{\pi\text{avg}}\) & 3 & 15,814 & 137.51/373/14 & 115 \\
10 & IN & \(\text{Intentional Injury/}\) & 4 & 16,161 & 96.77/593/17 & 167 \\
11 & DD & Drunk Driving/ & 4 & 29,610 & 148.05/274/99 & 200 \\
12 & TA & \(\text{Traffic Accident/}\) & 4 & 10,011 & 112.48/161/22 & 89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset statistics. The performance of the TJEE model and the baseline models on the Tibetan judicial event dataset is presented in Table 6. Specifically, F1_avg represents the average F1-score for both trigger detection and argument recognition. According to the experimental results in Table 6, the following conclusions can be drawn:  
1. In the first four benchmark experiments, the pre-trained models outperformed the randomly initialized models in both trigger detection and argument recognition. LSTM-pre-train achieved 5.64% and 7.99% higher F1-scores than LSTM-rand, while CNN-pre-train outperformed CNN-rand by 8.69% and 9.77%, respectively. This indicates that the CINO model pre-trained on a large-scale corpus can capture the deep information features of the text more efficiently due to learning a large amount of external knowledge. Therefore, its extraction performance is better than that of random vector initialization. 2. The overall performance of Bi-LSTM is improved compared to LSTM-pre-train, with F1-scores of 66.68% and 58.18% in trigger detection and argument recognition, respectively, and the F1_avg is improved by 3.69% compared to the latter. This is because Bi-LSTM integrates the semantic dependencies in the front and back directions in text feature extraction, which can provide richer information. 3. The TJEE model proposed in this study outperforms other models in both trigger detection and argument recognition, with the F1-score being 70.47% and 62.99% and F1_avg being 66.73%, which are 9.1%, 7.19%, and 8.11% better than CINO, and 16.6%, 16.42%, and 16.51% better than CNN-rand, respectively. It indicates that the model can adapt well to the long-text characteristics of Tibetan judicial text while also captur