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Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation
| Content Provider | MDPI |
|---|---|
| Author | Li, Pengpeng Luo, An Liu, Jiping Wang, Yong Zhu, Jun Deng, Yue Zhang, Junjie |
| Copyright Year | 2020 |
| 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. |
| Starting Page | 635 |
| e-ISSN | 22209964 |
| DOI | 10.3390/ijgi9110635 |
| Journal | ISPRS International Journal of Geo-Information |
| Issue Number | 11 |
| Volume Number | 9 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-26 |
| Access Restriction | Open |
| Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Transportation Science and Technology Chinese Address Element Bi-gru Neural Network Address Segmentation Viterbi |
| Content Type | Text |
| Resource Type | Article |
