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Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation

by 1,2, 2,3,*, 1,2, 1,2, 1, 4 and 3
1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
2
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, Beijing 100830, China
3
School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China
4
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 635; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110635
Received: 10 September 2020 / Revised: 16 October 2020 / Accepted: 21 October 2020 / Published: 26 October 2020
Chinese address element segmentation is a basic and key step in geocoding technology, and the segmentation results directly affect the accuracy and certainty of geocoding. However, due to the lack of obvious word boundaries in Chinese text, the grammatical and semantic features of Chinese text are complicated. Coupled with the diversity and complexity in Chinese address expressions, the segmentation of Chinese address elements is a substantial challenge. Therefore, this paper proposes a method of Chinese address element segmentation based on a bidirectional gated recurrent unit (Bi-GRU) neural network. This method uses the Bi-GRU neural network to generate tag features based on Chinese word segmentation and then uses the Viterbi algorithm to perform tag inference to achieve the segmentation of Chinese address elements. The neural network model is trained and verified based on the point of interest (POI) address data and partial directory data from the Baidu map of Beijing. The results show that the method is superior to previous neural network models in terms of segmentation performance and efficiency. View Full-Text
Keywords: Chinese address element; Bi-GRU neural network; address segmentation; Viterbi Chinese address element; Bi-GRU neural network; address segmentation; Viterbi
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MDPI and ACS Style

Li, P.; Luo, A.; Liu, J.; Wang, Y.; Zhu, J.; Deng, Y.; Zhang, J. Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation. ISPRS Int. J. Geo-Inf. 2020, 9, 635. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110635

AMA Style

Li P, Luo A, Liu J, Wang Y, Zhu J, Deng Y, Zhang J. Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation. ISPRS International Journal of Geo-Information. 2020; 9(11):635. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110635

Chicago/Turabian Style

Li, Pengpeng, An Luo, Jiping Liu, Yong Wang, Jun Zhu, Yue Deng, and Junjie Zhang. 2020. "Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation" ISPRS International Journal of Geo-Information 9, no. 11: 635. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110635

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