Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation
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 c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/11/635 |
| _version_ | 1797549718922854400 |
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| author | Pengpeng Li An Luo Jiping Liu Yong Wang Jun Zhu Yue Deng Junjie Zhang |
| author_facet | Pengpeng Li An Luo Jiping Liu Yong Wang Jun Zhu Yue Deng Junjie Zhang |
| author_sort | Pengpeng Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:19:25Z |
| format | Article |
| id | doaj.art-176ae5d4bd1e4e74935e5ab70661cb4b |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T15:19:25Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-176ae5d4bd1e4e74935e5ab70661cb4b2023-11-20T18:38:04ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-10-0191163510.3390/ijgi9110635Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element SegmentationPengpeng Li0An Luo1Jiping Liu2Yong Wang3Jun Zhu4Yue Deng5Junjie Zhang6Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaResearch Center of Government GIS, Chinese Academy of Surveying and Mapping, Beijing 100830, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, ChinaChinese 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.https://www.mdpi.com/2220-9964/9/11/635Chinese address elementBi-GRU neural networkaddress segmentationViterbi |
| spellingShingle | Pengpeng Li An Luo Jiping Liu Yong Wang Jun Zhu Yue Deng Junjie Zhang Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation ISPRS International Journal of Geo-Information Chinese address element Bi-GRU neural network address segmentation Viterbi |
| title | Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation |
| title_full | Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation |
| title_fullStr | Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation |
| title_full_unstemmed | Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation |
| title_short | Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation |
| title_sort | bidirectional gated recurrent unit neural network for chinese address element segmentation |
| topic | Chinese address element Bi-GRU neural network address segmentation Viterbi |
| url | https://www.mdpi.com/2220-9964/9/11/635 |
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