The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding
Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advance...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/9/572 |
| _version_ | 1797518964620787712 |
|---|---|
| author | Zheren Yan Can Yang Lei Hu Jing Zhao Liangcun Jiang Jianya Gong |
| author_facet | Zheren Yan Can Yang Lei Hu Jing Zhao Liangcun Jiang Jianya Gong |
| author_sort | Zheren Yan |
| collection | DOAJ |
| description | Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed. |
| first_indexed | 2024-03-10T07:36:43Z |
| format | Article |
| id | doaj.art-605cb833dd8a4b87a328cb0409f0684a |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T07:36:43Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-605cb833dd8a4b87a328cb0409f0684a2023-11-22T13:24:33ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-08-0110957210.3390/ijgi10090572The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text GeocodingZheren Yan0Can Yang1Lei Hu2Jing Zhao3Liangcun Jiang4Jianya Gong5School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaGeocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed.https://www.mdpi.com/2220-9964/10/9/572geocodingdeep learningnamed entity disambiguationplace name resolution |
| spellingShingle | Zheren Yan Can Yang Lei Hu Jing Zhao Liangcun Jiang Jianya Gong The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding ISPRS International Journal of Geo-Information geocoding deep learning named entity disambiguation place name resolution |
| title | The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding |
| title_full | The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding |
| title_fullStr | The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding |
| title_full_unstemmed | The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding |
| title_short | The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding |
| title_sort | integration of linguistic and geospatial features using global context embedding for automated text geocoding |
| topic | geocoding deep learning named entity disambiguation place name resolution |
| url | https://www.mdpi.com/2220-9964/10/9/572 |
| work_keys_str_mv | AT zherenyan theintegrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT canyang theintegrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT leihu theintegrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT jingzhao theintegrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT liangcunjiang theintegrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT jianyagong theintegrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT zherenyan integrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT canyang integrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT leihu integrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT jingzhao integrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT liangcunjiang integrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding AT jianyagong integrationoflinguisticandgeospatialfeaturesusingglobalcontextembeddingforautomatedtextgeocoding |