## 4 Evaluation Metrics and Experimental Results
### Experimental Results and Analysis
#### 4.3.2 Analysis of Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Example** & **Processing of Address Elements** \\ \hline \multirow{2}{*}{\begin{tabular}{c} CHTopoNER + FSM \\ \end{tabular} } & No. 132 Wangwu Road, & \multirow{2}{*}{Invalid address} \\  & Zheng Shang Ming Zuan & \\ \hline \multirow{2}{*}{\begin{tabular}{c} CHTopoNER + bidirectional FSM \\ \end{tabular} } & No. 132 Wangwu Road, & \multirow{2}{*}{\begin{tabular}{c} Wangwu Road, Zheng Shang Ming Zuan \\ (JD1)/No. 132 (MP1) \\ \end{tabular} } \\  & Zheng Shang Ming Zuan & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **CHTopoNER + Algorithm 1** \\ **(dynamic FSM)** \\ \end{tabular} } & **No. 132 Wangwu Road, & \multirow{2}{*}{\
\begin{tabular}{c} **Wangwu Road (JD1)/No. 132 (MP1)/Zheng** \\ **Sheng Ming Zuan (MP2)** \\ \end{tabular} } \\  & **
\begin{tabular}{c} **Wangwu Road (JD1)/No. 132 (MP1)/Zheng** \\ **[PERSON] (MP2)** \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Specific experimental results of the CHTopoNER model with added FSM, bidirectional FSM, and Algorithm 1 (dynamic FSM) to non-standard address data. metrics (P, R, and F1). In comparison with the baseline model BiLSTM-CRF, the CHTopoNER model exhibits improvements of 0.9% in P, 7.5% in R, and 10.2% in F1 values. Compared to the state-of-the-art BERT-BiLSTM-CRF model, the CHTopoNER model achieves improvements of 1.0% in P, 7.0% in R, and 2.6% in F1 values. The CHTopoNER model proposed here exhibited the best evaluation metrics in the COVID-19 ESD dataset: P, R, and F1 (Table 2). Compared with the baseline model BiLSTM-CRF, P, R, and F1 improved by 3.1%, 4.6%, and 3.9%, respectively. Compared with the state-of-the-art BERT-BiLSTM-CRF model, the three evaluation metrics improved by 0.2%, 1.1%, and 0.7%, respectively. To further compare and analyze the experimental results of our CHTopoNER model with other models on the PFR, MSRA, and COVID-19 ESD datasets, we present the experimental outcomes graphically in Figure 5.  
Figure 5 shows that the proposed CHTopoNER model, compared to the other models, demonstrates superior experimental performance across the three distinct datasets. This can be attributed to the utilization of the Chinese-roberta-wwm-ext pretraining with a Chinese full-word masking strategy and the improved SoftLexicon approach to capture character-level and word-level information from input texts. Furthermore, the integration of the TCNN layer in semantic feature extraction enables the consideration of both local and global semantic information, thus minimizing the loss of semantic features. However, the performance of the CHTopoNER model on the COVID-19 ESD dataset is slightly inferior to that on the PFR and MSRA datasets. This discrepancy arises from the fact that while the PFR and MSRA datasets focus on recognizing a single entity type, namely geographical places, the COVID-19 ESD dataset involves the recognition of 16 different types of address element entities. Typically, the presence of multiple entity types may increase data sparsity and context ambiguity, rendering it challenging for the model to capture subtle features of each entity type and accurately determine the correct entity category. Consequently, the precision of the CHTopoNER model's experimental results on the COVID-19 ESD dataset is marginally lower. The F1 values of different types of address element entities are shown in Figure 4. Among them, the JD1 type exhibits the highest F1 value, while the POI2 type has the lowest F1 value. This discrepancy can be attributed to the higher frequency and distinct features of JD1 entities in the text, such as terms like \"XX Road\" or \"XX Avenue\". Conversely, the lower F1 value for the POI2 type stems from the less prominent textual features associated with this type and its infrequent appearance in the text, with examples like  
Figure 5: Experimental results of the six models on the three different datasets. \"Cha Bai Dao\" or \"Wen Xuan Art Studio\".