## 4 Conclusions  
In this study, we built the ASM with strong address semantic understanding using a pretraining approach to semantic modelling of a vast and complicated address corpus. We introduced a fine-tuning approach in deep transfer learning to achieve a high accuracy of semantic address matching. The main conclusions of this study are as follows:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Comparison Methods**} & \multicolumn{3}{c}{**Predictive Results**} \\ \cline{2-4}  & **Precision** & **Recall** & **F1 Score** \\ \hline Levenshtein distance + SVM & 0.849 & 0.795 & 0.822 \\ Levenshtein distance + RF & 0.875 & 0.819 & 0.847 \\ Jaccard similarity coefficient + SVM & 0.890 & 0.881 & 0.885 \\ Jaccard similarity coefficient + RF & 0.914 & 0.911 & 0.912 \\ Jaro similarity + SVM & 0.902 & 0.807 & 0.854 \\ Jaro similarity + RF & 0.891 & 0.858 & 0.874 \\ word2 vec + SVM & 0.832 & 0.762 & 0.797 \\ word2 vec + RF & 0.910 & 0.879 & 0.895 \\ CRF + word2 vec + SVM & 0.826 & 0.751 & 0.788 \\ CRF + word2 vec + RF & 0.895 & 0.846 & 0.871 \\ word2 vec + ESIM & 0.969 & 0.961 & 0.965 \\ DSAMM & 0.987 & 0.982 & 0.984 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative evaluations of different address matching methods in precision, recall, and F1 score.  
Figure 7: Comparative evaluations of different address matching methods in F1 score.  
The ASM was constructed using a self-supervised pretrained language model. The experimental results demonstrate that the ASM can achieve a high level of accuracy with the objective of predicting unknown characters.  
The DSAMM was constructed using the fine-tuning approach in deep transfer learning. The results of the comparison experiments showed that the DSAMM performed the best, with all the metrics above 0.98.  
It is shown that utilizing deep transfer learning, high address matching accuracy can be attained with only a few labelled training datasets.  
However, this study has some limitations. (1) We treated each address record as a sentence, which resulted in the hierarchy of addresses being ignored; (2) our experimental area was limited to a city, which ignored the ambiguity of place names. Therefore, in a future work, we will consider, first, studying how to ensure the accuracy of address matching when there are semantic similarities or ambiguities in addresses at a larger regional scale, and, second, trying to introduce geographical information associated with addresses and learning external knowledge to assist in achieving better address matching.  
Conceptualization, L.X. and F.X.; methodology, L.X., C.Z. and R.M.; validation, X.X. and X.Z.; formal analysis, L.X., F.X. and R.M.; data curation, Y.W.; writing--original draft preparation, L.X.; writing--review and editing, L.X., F.X. and X.Z.; visualization, X.X.; supervision, F.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Grant Nos. 42001354, 42050103; Natural Science Foundation of Zhejiang Province Grant Nos.LGG22D010001, LQ19D01011, LGN22D010003; Scientific Research Fund of Zhejiang Provincial Education Department Grant No. Y202147221; and Scientific Research and Development Foundation of Zhejiang A&F University Grant No. 2020 FR060, 2020 FR064.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors would like to thank the editor and the reviewers for their contributions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.