{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:17:53Z","timestamp":1781587073128,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T00:00:00Z","timestamp":1607731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics","award":["19-050-11-04"],"award-info":[{"award-number":["19-050-11-04"]}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["NO. 2020211"],"award-info":[{"award-number":["NO. 2020211"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0803106"],"award-info":[{"award-number":["2016YFC0803106"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Location services based on address matching play an important role in people\u2019s daily lives. However, with the rapid development of cities, new addresses are constantly emerging. Due to the untimely updating of word segmentation dictionaries and address databases, the accuracy of address segmentation and the certainty of address matching face severe challenges. Therefore, a new address element recognition method for address matching is proposed. The method first uses the bidirectional encoder representations from transformers (BERT) model to learn the contextual information and address model features. Second, the conditional random field (CRF) is used to model the constraint relationships among the tags. Finally, a new address element is recognized according to the tag, and the new address element is put into the word segmentation dictionary. The spatial information is assigned to it, and it is put into the address database. Different sequence tagging models and different vector representations of addresses are used for comparative evaluation. The experimental results show that the method introduced in this paper achieves the maximum generalization ability, its F1 score is 0.78, and the F1 score on the testing dataset also achieves a high value (0.95).<\/jats:p>","DOI":"10.3390\/ijgi9120745","type":"journal-article","created":{"date-parts":[[2020,12,13]],"date-time":"2020-12-13T20:56:57Z","timestamp":1607893017000},"page":"745","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Recognition Method of New Address Elements in Chinese Address Matching Based on Deep Learning"],"prefix":"10.3390","volume":"9","author":[{"given":"Hongwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiting","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renfei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-6837","authenticated-orcid":false,"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4615-2029","authenticated-orcid":false,"given":"Qingyun","family":"Du","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1080\/13658810802084806","article-title":"Address databases for national SDI: Comparing the novel data grid approach to data harvesting and federated databases","volume":"23","author":"Coetzee","year":"2009","journal-title":"Int. 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