## 4 Evaluation Metrics and Experimental Results
### Experimental Results and Analysis  
#### 4.3.1 Experimental Results  
To verify the effectiveness of the method proposed in this study, the BiLSTM-CRF [50], IDCNN-CRF [51], BiLSTM-attention-CRF [52], BERT-BiLSTM-CRF [53], and CHTopoNER models were applied to the chosen dataset. Furthermore, FSM, bidirectional FSM, and dynamic FSM were added to the experiments based on the CHTopoNER model.  
Experimental results of geographical placename recognition using different models on the PFR dataset are presented in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline
**Component** & **Details** \\ \hline Central Processing Unit (CPU) & Intel(R) Core(TM) i9-12900H \\ Graphics Card (GPU) & NVIDIA GeForce RTX 3080 Ti \\ Operating System & Ubuntu 18.04 \\ Programming Language & Python 3.7 \\ Deep Learning Framework & TensorFlow1.14.0 \\ \hline \end{tabular}
\end{table}
Table 2: Software and hardware used in our investigation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline BiLSTM-CRF (baseline) & 0.990 & 0.890 & 0.864 \\ \hline IDCNN-CRF1 & 0.994 & 0.889 & 0.864 \\ \hline IDCNN-CRF2 & 0.995 & 0.916 & 0.875 \\ \hline BiLSTM-Attention-CRF & 0.989 & 0.840 & 0.840 \\ \hline BERT-BiLSTM-CRF & 0.989 & 0.895 & 0.940 \\ \hline
**CHTopoNER** & **0.999** & **0.965** & **0.981** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the MSRA dataset across different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline BiLSTM-CRF (baseline) & 0.912 & 0.774 & 0.837 \\ \hline IDCNN-CRF1 & 0.992 & 0.773 & 0.869 \\ \hline IDCNN-CRF2 & 0.994 & 0.790 & 0.883 \\ \hline BiLSTM-Attention-CRF & 0.938 & 0.740 & 0.827 \\ \hline BERT-BiLSTM-CRF & 0.996 & 0.891 & 0.940 \\ \hline
**CHTopoNER** & **0.997** & **0.953** & **0.975** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the PFR dataset across different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline BiLSTM-CRF (baseline) & 0.990 & 0.890 & 0.864 \\ \hline IDCNN-CRF1 & 0.994 & 0.889 & 0.864 \\ \hline IDCNN-CRF2 & 0.995 & 0.916 & 0.875 \\ \hline BiLSTM-Attention-CRF & 0.989 & 0.840 & 0.840 \\ \hline BERT-BiLSTM-CRF & 0.989 & 0.895 & 0.940 \\ \hline
**CHTopoNER** & **0.999** & **0.965** & **0.981** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the MSRA dataset across different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline BiLSTM-CRF (baseline) & 0.961 & 0.710 & 0.817 \\ \hline IDCNN-CRF1 & 0.976 & 0.736 & 0.839 \\ \hline IDCNN-CRF2 & 0.988 & 0.729 & 0.839 \\ \hline BiLSTM-Attention-CRF & 0.936 & 0.703 & 0.809 \\ \hline BERT-BiLSTM-CRF & 0.989 & 0.735 & 0.843 \\ \hline
**CHTopoNER** & **0.991** & **0.743** & **0.849** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Address element recognition by different models in the COVID-19 ESD social media dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline CHTopoNER + FSM & 0.777 \\ CHTopoNER + bidirectional FSM & 0.781 \\
**CHTopoNER + Algorithm 1 (dynamic FSM)** & **0.839** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracies of the CHTopoNER models with added FSM, bidirectional FSM, and Algorithm 1 (dynamic FSM) to standard address data.  
Figure 4: Experimental results of different types of address element entities extracted by the CHTopoNER model on the COVID-19 ESD social media dataset.