# Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification
## 5 Conclusions and Future Works  
This study proposes a text mining method combining the pre-trained BERT and a R-GCN to automatically explore accident causal information in accident investigation reports. The proposed method avoids the tedious preprocessing steps of previous text mining methods and extracts text features by employing a pre-trained BERT to embed words from text reports into a high-dimensional vector space. Then, by using a R-GCN with \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & \\ \hline \hline \multirow{11}{*}{Ground truth} & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & Total & TP & FN & Recall \\  & 1 & 9 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 12 & 9 & 3 & 0.75 \\  & 2 & 0 & 34 & 0 & 1 & 4 & 0 & 1 & 0 & 0 & 0 & 0 & 40 & 34 & 6 & 0.85 \\  & 3 & 1 & 0 & 39 & 0 & 0 & 0 & 1 & 0 & 0 & 8 & 1 & 50 & 39 & 11 & 0.78 \\  & 4 & 0 & 0 & 1 & 12 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 15 & 12 & 3 & 0.80 \\  & 5 & 0 & 2 & 0 & 0 & 19 & 0 & 0 & 0 & 0 & 2 & 0 & 23 & 19 & 4 & 0.83 \\  & 6 & 0 & 1 & 0 & 2 & 0 & 3 & 0 & 1 & 1 & 1 & 0 & 9 & 3 & 6 & 0.33 \\  & 7 & 0 & 1 & 0 & 0 & 0 & 0 & 5 & 0 & 0 & 0 & 0 & 6 & 5 & 1 & 0.83 \\  & 8 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 7 & 0 & 0 & 2 & 10 & 7 & 3 & 0.70 \\  & 9 & 0 & 0 & 0 & 2 & 2 & 2 & 0 & 15 & 0 & 0 & 21 & 15 & 6 & 0.71 \\  & 10 & 0 & 0 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 6 & 0 & 10 & 6 & 4 & 0.60 \\  & 11 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 3 & 4 & 3 & 1 & 0.75 \\ \hline \multirow{11}{*}{Ground truth} & Total & 10 & 39 & 45 & 16 & 27 & 5 & 9 & 9 & 16 & 18 & 6 & 200 & & & \\  & TP & 9 & 34 & 39 & 12 & 19 & 3 & 5 & 7 & 15 & 6 & 3 & & & & \\ \cline{1-1}  & FP & 1 & 5 & 6 & 4 & 8 & 2 & 4 & 2 & 1 & 12 & 3 & & & & \\ \cline{1-1}  & Precision & 0.90 & 0.87 & 0.87 & 0.75 & 0.70 & 0.60 & 0.56 & 0.78 & 0.94 & 0.33 & 0.50 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Corresponding procedure for calculating the average F1-score of Fold 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Average Precision** & **Average Recall** & **AvgF1-Score** \\ \hline Decision trees & 0.48 & 0.55 & 0.51 \\ KNN & 0.49 & 0.52 & 0.50 \\ Naive Bayes & 0.57 & 0.54 & 0.55 \\ Logistic regression & 0.47 & 0.87 & 0.61 \\ LSTM & 0.58 & 0.64 & 0.61 \\ GRU & 0.70 & 0.61 & 0.65 \\ SGRU & 0.73 & 0.69 & 0.71 \\ \hline Ours w/o gate mechanism & 0.74 & 0.72 & 0.73 \\ Ours & 0.79 & 0.76 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the comparison experiment and the ablation experiment. a gate mechanism, the syntactic structure in the text is also processed into high-dimensional vectors. By concatenating these two features and with the help of the classifier, it is possible to understand both the word and syntax of the accident investigation reports. Compared with methods such as text frequency alone, it is more accurate and concise. Compared with retraining BERT to extract text and syntax features at the same time, it is very cheap and fast. The experimental results show that the proposed method could achieve an average F1-score as high as 0.77, which exceeds existing methods and has important practical significance for accident causal classification. However, it is undeniable that, although the existing methods have made certain breakthroughs compared with previous studies, the classification accuracy still needs to be improved. The methods proposed at present can only assist in the analysis of accident causes from accident investigation reports to a certain extent and cannot completely replace experienced experts. Especially in accident cause analyses, once the accident causal classification is wrong, it may bring unnecessary investment or mislead accident prevention. This is a key breakthrough in future work. In the future, the accuracy of accident causal classification can be improved by enriching the accident causal dataset and adding relevant labels. At the same time, on the basis of this study, we can further explore the BERT-based text information encoding method to build a more efficient expert system.