| Title |
Deep Contrast Learning Approach for Address Semantic Matching |
| ID_Doc |
39816 |
| Authors |
Chen, J; Chen, JP; She, XR; Mao, J; Chen, G |
| Title |
Deep Contrast Learning Approach for Address Semantic Matching |
| Year |
2021 |
| Published |
Applied Sciences-Basel, 11, 16 |
| DOI |
10.3390/app11167608 |
| 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. |
| Author Keywords |
address matching; smart city; contrast learning; neural networks; data augmentation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000688698200001 |
| WoS Category |
Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied |
| Research Area |
Chemistry; Engineering; Materials Science; Physics |
| PDF |
https://www.mdpi.com/2076-3417/11/16/7608/pdf?version=1629697926
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