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Article

The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding

School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(9), 572; https://doi.org/10.3390/ijgi10090572
Received: 6 July 2021 / Revised: 19 August 2021 / Accepted: 20 August 2021 / Published: 24 August 2021
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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. View Full-Text
Keywords: geocoding; deep learning; named entity disambiguation; place name resolution geocoding; deep learning; named entity disambiguation; place name resolution
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MDPI and ACS Style

Yan, Z.; Yang, C.; Hu, L.; Zhao, J.; Jiang, L.; Gong, J. The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding. ISPRS Int. J. Geo-Inf. 2021, 10, 572. https://doi.org/10.3390/ijgi10090572

AMA Style

Yan Z, Yang C, Hu L, Zhao J, Jiang L, Gong J. The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding. ISPRS International Journal of Geo-Information. 2021; 10(9):572. https://doi.org/10.3390/ijgi10090572

Chicago/Turabian Style

Yan, Zheren, Can Yang, Lei Hu, Jing Zhao, Liangcun Jiang, and Jianya Gong. 2021. "The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding" ISPRS International Journal of Geo-Information 10, no. 9: 572. https://doi.org/10.3390/ijgi10090572

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