# Deep Contrast Learning Approach for Address Semantic Matching
###### Abstract  
Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines. address matching; smart city; contrast learning; neural networks; data augmentation +
Footnote †: journal: Medical  
forward corresponding solutions to the extraction of address elements: some rule-based and fuzzy Chinese address coding methods are raised to establish a standard address database and generate matching rules [11; 12; 13]. However, this method relies on the completeness of address database, and it is difficult to formulate all the rules as the growth of database. [PERSON] proposed an optimized Chinese address matching model and provided a coding service with this model [14]. [PERSON] used Bert as pretrained embedding model and applied CRF algorithm to extract address element and semantic features [15]. Comphed word2 vec into CRF to convert address elements into a fixed dimension semantic representation vector [16]. Nevertheless, semantic information cannot be obtained effectively when dealing with longer address records and the distribution of information density is uneven. To address this kind of problem, follow-up research begins to apply a neural network to do this task, such as using CNN or RNN [17]. [PERSON] proposed a multi-model fusion approach which apply RNN and GRU as address semantic information modeling [18]. This model has achieved a good performance improvement compared to single similarity measurement-based models and some supervised learning methods. Next, [PERSON] combined the advantages of RNN and CNN models, proposed the RCNN model, which uses a bidirectional structure and embeds BiRNN structure into convolutional layer [19]. This kind of structure effectively reduces the network noise and maximizes the ability to extract the context information of addresses. In the field of smart cities, there are also many researchers trying to introduce deep learning to cope with city development issues, such as the cities expansion and personalized POI recommendation [20; 21]. [PERSON] proposed a geographic analysis system for NER recognition, which can efficiently sort out geographic analysis problems [22]. After that, the deep learning methods are verified to solve the spatial data and the urban geographic problems, and prove that deep learning ways are the most effective way and have broad application prospects [23]. [PERSON] have analyzed more than 140 articles about using artificial neural networks to settle the urban geographic problems. In summary, artificial neural networks have obvious advantages over traditional methods [24]. The semantic representation of addresses based on deep learning is essentially an NLP problem [25; 26]. In the field of NLP, contrastive learning algorithms have recently been widely proposed [27; 28]. Contrastive learning algorithm is a subset of deep learning. Its goal is to bring the enhanced new samples as close as possible in the embedding space and make different samples as far away as possible.