Abstract: The standardization of Chinese geographic addresses plays a crucial role in the current construction of smart cities. The traditional geographic address standardization technology usually uses the methods of similarity calculation or rule base matching based on the text character level, and the processing effect of complex, special or redundant addresses is poor. This paper proposes an address match- ing algorithm that combines attention mechanism and multi-level representation by converting the address standardization task into a matching degree calculation task for similar addresses. Firstly, according to the special grammatical structure of the address text, a standard address tree is constructed by using the Trie grammatical tree. Secondly, based on the attention mechanism, the Bi-LSTM network and the CNN network are used to generate multi-level semantic representations of address pairs. Finally, the similarity is calculated by Manhattan distance. On the self-built dataset, the proposed SGAM (Symmetrical Geographic Address Matching) model improves the matching accuracy (91.22%) by 4%~10% in comparison to TextRCNN, FastText, attention-based convolutional neural network (ABCNN) and other models, proving that the SGAM model has better performance on the address matching task.  

Key words: geographic address, text similarity calculation, attention mechanism, hybrid neural network, smart city