{"title":"带地理预训练模型的中文地名匹配深度神经网络模型","authors":"Qinjun Qiu, Shiyu Zheng, Miao Tian, Jiali Li, Kai Ma, Liufeng Tao, Zhong Xie","doi":"10.1080/17538947.2024.2353111","DOIUrl":null,"url":null,"abstract":"Multiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that shar...","PeriodicalId":54962,"journal":{"name":"International Journal of Digital Earth","volume":"46 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep neural network model for Chinese toponym matching with geographic pre-training model\",\"authors\":\"Qinjun Qiu, Shiyu Zheng, Miao Tian, Jiali Li, Kai Ma, Liufeng Tao, Zhong Xie\",\"doi\":\"10.1080/17538947.2024.2353111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that shar...\",\"PeriodicalId\":54962,\"journal\":{\"name\":\"International Journal of Digital Earth\",\"volume\":\"46 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Digital Earth\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/17538947.2024.2353111\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Digital Earth","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/17538947.2024.2353111","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
A deep neural network model for Chinese toponym matching with geographic pre-training model
Multiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that shar...
期刊介绍：
The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that:
Progress visions for Digital Earth frameworks, policies, and standards;
Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery;
Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared;
Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level;
Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning;
Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and
Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.
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