## 3 Results and Discussion
### Comparative Experiment Analysis of the Address Matching  
To evaluate the semantic address matching performance of the DSAMM we proposed, several baseline models were selected for comparison. The methods compared include character-based matching methods, machine-learning-based matching methods, and deep-learning-based matching methods, where the character-based matching methods in this paper use Levenshtein distance [8], Jaccard similarity coefficient [11], and Jaro similarity [12] to measure string correlation, followed by a random forest (RF) classifier [59] and a support vector machine (SVM) classifier [60] to determine whether the address pairs match. In terms of comparing machine-learning-based matching methods, we compared with the method proposed by [PERSON] et al. [38]. It uses CRF to label the address elements, then uses word2 vec for embedding, and applies RF and SVM for classification prediction. In terms of deep-learning-based matching methods, we compared with the method proposed by [PERSON] et al. [21]. It uses a two-stage model, with the first stage using word2 vec to obtain embeddings of address pairs for use as input to the next stage of the deep neural network, and the second stage using a typical deep learning model for interaction-based text matching, ESIM [47], which is directly for address matching.  
The results of the comparison of the three metrics for each method are shown in Table 6. As the F1 score is the most representative and important evaluation metric, we present the F1 scores for each method in the form of a bar chart for a more visual comparison. As shown in Figure 7, the Jaccard similarity coefficient stands out when using a string-similarity-based approach for the semantic address matching. The classifier employing RF as the task regularly outperforms SVM, and this is also true for the other two groups using machine learning techniques, according to performance comparisons.  
When comparing the machine-learning-based matching methods, the CRF method performs worse, probably because the pretraining corpus is more susceptible to influence. In addition, the performance of the machine-learning-based matching methods did not outperform the string-based matching methods, which may be since [PERSON] et al. used relatively generic English addresses, whereas Chinese addresses have their own uniqueness and, therefore, lead to different conclusions [38].  
Figure 6: Comparison of the precision of the validation set of the DSAMM instances with different scaled training sets with labels.  
The deep learning method of \"word2 vec + ESIM\" outperformed the string-similarity-based and machine-learning-based methods in a comparison of results, with all the three evaluation metrics having values above 0.96. This shows how the deep learning framework can significantly increase the accuracy of the semantic address matching. All the final metric values attained by training the DSAMM instances in our study were above 0.98, with the F1 value coming in at 0.984, which represents a notable improvement in prediction evaluation values and the best metrics. This suggests that the migration learning model used in this work can effectively increase the accuracy of semantic address matching. Additionally, we constructed the model without segmenting the address text for elements, which significantly increased the effectiveness of address matching.