## 4 Experimental Setup and Results
### Experimental Data
#### 4.4.4 Experimental Result of Sequence Labeling SBD  
This study conducts sequence labeling experiments on HMM, CRF, Bi-LSTM, and Bi-LSTM-CRF models. The experimental results are shown in Table 10. A, P, R, and F1 represent the accuracy, precision, recall, and F1 score.  
Table 10 shows the SBD results based on the sequence labeling methods. We know that when the sequence labeling methods for SBD are trained, most syllables are labeled with label M, so there is a problem of label imbalance. We can see from Table 10 that the F1 of label M is the highest among the four models, followed by label E, and label B is the worst. It can be seen from Table 4 that the average F1 of the three labels under the four models ranges from 98.82% to 99.63%, but the accuracy of the three labels varies greatly. Since label M is the most significant proportion of labels in the training data, its F1 is the highest, ranging from 99.34% to 99.81%, label B from 82.38% to 94.67%, and label E from 85.7 to 94.68%. This study focuses on SBD, and we should pay more attention to the evaluation index of the label E. For the metric of label E, CRF and the Bi-LSTM-CRF have the best effect among the four models, and their performance is approximately 94.68%, followed by Bi-LSTM, which is 86.08%, and the F1 of HMM is the lowest, which is 85.7%. It can be seen that among the sequence labeling methods based on deep learning, the CRF Method is better than Bi-LSTM, and Bi-LSTM-CRF is much higher than Bi-LSTM. We can conclude that CRF can improve the performance of sequence labeling SBD. It can be seen that for the sequence labeling method to realize SBD, the evaluation results show significant differences among labels due to the imbalance of labels.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**HMM**} & \multicolumn{4}{c}{**CRF**} & \multicolumn{4}{c}{**Bi-LSTM**} & \multicolumn{4}{c}{**Bi-LSTM-CRF**} \\ \hline
**Labels** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline B & 81.22 & 91.59 & 86.10 & 95.27 & 94.07 & 94.67 & 71.60 & 97.00 & 82.38 & 90.53 & 96.91 & 93.61 \\ E & 81.14 & 90.79 & 85.70 & 95.27 & 94.09 & 94.68 & 77.78 & 96.37 & 86.08 & 90.52 & 96.85 & 93.58 \\ M & 99.68 & 99.24 & 99.46 & 99.79 & 99.83 & 99.81 & 99.88 & 98.81 & 99.34 & 99.89 & 99.63 & 99.76 \\ Avg/Total & 99.04 & 98.96 & 98.99 & 99.63 & 99.63 & 99.63 & 99.00 & 98.74 & 98.82 & 99.56 & 99.54 & 99.55 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental result of sequence labeling SBD.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**True Positive**} & \multicolumn{2}{c}{**Window = 3**} & \multicolumn{2}{c}{**Window = 5**} & \multicolumn{2}{c}{**Window = 10**} & \multicolumn{2}{c}{**Window = 15**} \\ \cline{2-9}  & **BERT** & **[PERSON]** & **BERT** & **BERT** & **BERT** & **[PERSON]** & **[PERSON]** & **BERT** & **BERT** \\  & **(BPE)** & **[PERSON]** & **(BPE)** & **(BPE)** & **(BPE)** & **[PERSON]** & **(BPE)** & **(BPE)** & **[PERSON]** \\ \hline BERT & 42,553 & 40,128 & 41,693 & 40,909 & 43,071 & 40,388 & 43,655 & 39,589 \\ BERT-CNN & 42,175 & 40,409 & 41,815 & 40,629 & 43,548 & 40,316 & 42,188 & 39,995 \\ BERT-RNN & 42,145 & 40,259 & 42,333 & 40,205 & 42,564 & 39,985 & 43,666 & 41,365 \\ BERT-RCNN & 42,677 & 40,537 & 42,044 & 40,915 & 42,849 & 40,466 & 42,847 & 40,921 \\ BERT-DPCNN & 41,790 & 40,431 & 42,880 & 40,835 & 42,683 & 40,579 & 42,236 & 40,290 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ture Positive samples of Tibetan SBD based on BERT(BPE) and TiBERT with four window sizes.