## 1 Introduction
The aforementioned techniques, however, frequently struggle when dealing with nonstandardized (missing address elements or represented by POI) and complexly structured addresses (such as the Chinese address feature aforementioned). In recent years, the area of artificial intelligence has seen tremendous progress in natural language processing (NLP), most of which is attributable to deep learning's enhanced performance. Word2 vec [29], ELMo [30], GPT [31], BERT [32], XLNet [33], ERNIE [34], and ELECTRA [35] are a few of these classical language models. Since addresses as special textual descriptions, more and more studies in address matching has also introduced natural language models based on deep learning [36]. [PERSON] et al. analyzed 41 papers on address matching published between 2002 and 2021 and discovered that most of the relevant studies have used deep learning methods. Among them, consistent with the above in this paper, due to the complexity of Chinese addresses, Chinese address matching accounted for half of the studies [37]. [PERSON] et al. used CRF and word2 vec for address matching to extract the semantics of addresses without designing complex rules [38]; [PERSON] et al. provides a convolutional neural network (W-TextCNN) for Chinese address pattern classification [39]. With the popularity of gating mechanism neural networks, address matching and normalizing based on LSTM and GRU have been carried out by an increasing number of researchers [40; 41; 42; 43]. [PERSON] et al. used a deep neural network based on bidirectional GRUs for place name matching [44]; [PERSON] enriched the address context by collecting address data on the Internet and trained an address representation model with two LSTMs and attention mechanisms to extract address vectors [45]. While [PERSON] et al. incorporated the hierarchical relationship between address elements into a neural network and proposed a BiLSTM-based multitasklearning method [46], [PERSON] et al. proposed a contrast learning address matching model based on attention-Bi-LSTM-CNN networks (ABLC) [47]. Subsequently, more and more researchers have used the attention mechanism in their address matching models [48; 49; 50]. With the popularity of pretrained language models, [PERSON] et al. used the classical enhanced sequence inference model (ESIM) [51] for address record pair modelling [21], whereas [PERSON] et al. and [PERSON] et al. used the BERT model. [PERSON] et al. proposed a BERT-based model for extracting address semantic representations to achieve the fusion of address semantics and geospatial information [36]; [PERSON] et al. combined BERT and LSTM, and proposed a hierarchical region-based approach for geolocation of Chinese addresses [52]. However, all of the aforementioned methods require the extraction of address semantic features to embedding, and this can affect the effectiveness of address semantic understanding, as has been demonstrated in the field of NLP [32]. In summary, when dealing with nonstandardized addresses with complicated structures, the aforementioned approaches still lack a level of comprehension of address semantics, which negatively impacts the accuracy of address matching. To address the above problems, we use a deep transfer learning approach. First, we pretrain an addresses corpus so that our address semantic model (abbreviated as ASM) can learn unsupervised address contexts to better understand address semantics. Then, we use the address-specific geospatial property to build a labelled address matching dataset, allowing the matching problem to be converted into a binary classification prediction problem. Finally, fine-tuning the ASM with the address matching dataset allows the model to improve its performance significantly. The contributions of this paper are as follows: (1) A neural network based on a multihead self-attention mechanism and a permutation-based target task is used to train the ASM for a large-scale corpus in an unsupervised automated manner. The ASM can learn address semantics better.