{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T05:19:38Z","timestamp":1765775978324,"version":"3.48.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"A Project Supported by Scientific Research Fund of Hunan Provincial Education Department","award":["22A0341"],"award-info":[{"award-number":["22A0341"]}]},{"name":"Science and Technology Innovation Program of Hunan Province","award":["2023SK2081"],"award-info":[{"award-number":["2023SK2081"]}]},{"name":"the Hunan Province Degree and Postgraduate Teaching Reform Research Project","award":["2023JGYB193"],"award-info":[{"award-number":["2023JGYB193"]}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["No. 24YJAZH237"],"award-info":[{"award-number":["No. 24YJAZH237"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Geogr Syst"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s10109-025-00482-3","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T06:21:37Z","timestamp":1763965297000},"page":"555-583","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A semi-supervised Chinese toponym recognition methods combining active learning and self-training"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8356-5474","authenticated-orcid":false,"given":"Yijiang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuhua","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"482_CR1","doi-asserted-by":"crossref","unstructured":"Ahlers D (2013) Assessment of the accuracy of GeoNames gazetteer data. In: Proceedings of the 7th workshop on geographic information retrieval. Association for Computing Machinery, New York, NY, USA, pp 74\u201381","DOI":"10.1145\/2533888.2533938"},{"key":"482_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102139","volume":"57","author":"B Alkouz","year":"2020","unstructured":"Alkouz B, Al Aghbari Z (2020) SNSJam: road traffic analysis and prediction by fusing data from multiple social networks. Inf Process Manag 57:102139. https:\/\/doi.org\/10.1016\/j.ipm.2019.102139","journal-title":"Inf Process Manag"},{"key":"482_CR3","unstructured":"Al-Olimat H, Thirunarayan K, Shalin V, Sheth A (2018) Location name extraction from targeted text streams using gazetteer-based statistical language models. Proc 27th Int Conf Comput Linguist 1986\u20131997"},{"key":"482_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.osnem.2021.100134","volume":"23","author":"S Andreadis","year":"2021","unstructured":"Andreadis S, Antzoulatos G, Mavropoulos T et al (2021) A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets. Online Soc Netw Media 23:100134. https:\/\/doi.org\/10.1016\/j.osnem.2021.100134","journal-title":"Online Soc Netw Media"},{"key":"482_CR5","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1109\/TNN.2009.2015974","volume":"20","author":"O Chapelle","year":"2009","unstructured":"Chapelle O, Scholkopf B, Zien A (2009) Semi-supervised learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]. IEEE Trans Neural Netw 20:542\u2013542. https:\/\/doi.org\/10.1109\/TNN.2009.2015974","journal-title":"IEEE Trans Neural Netw"},{"key":"482_CR6","doi-asserted-by":"crossref","unstructured":"Clark K, Luong M-T, Manning CD, Le Q (2018) Semi-Supervised sequence modeling with cross-view training. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds) Proceedings of the 2018 conference on empirical methods in natural language processing. Association for Computational Linguistics, Brussels, Belgium, pp 1914\u20131925","DOI":"10.18653\/v1\/D18-1217"},{"key":"482_CR7","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-319-77113-7_3","volume-title":"Computational linguistics and intelligent text processing","author":"V Claveau","year":"2018","unstructured":"Claveau V, Kijak E (2018) Strategies to select examples for active learning with conditional random fields. Computational linguistics and intelligent text processing. Springer, Cham, pp 30\u201343"},{"key":"482_CR8","doi-asserted-by":"crossref","unstructured":"Culotta A, McCallum A (2005) Reducing labeling effort for structured prediction tasks. In: Proceedings of the 20th national conference on Artificial intelligence - Vol 2. AAAI Press, Pittsburgh, Pennsylvania, pp 746\u2013751","DOI":"10.21236\/ADA440382"},{"key":"482_CR9","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, Vol 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171\u20134186"},{"key":"482_CR10","first-page":"136","volume":"19","author":"NJ Fern\u00e1ndez Mart\u00ednez","year":"2020","unstructured":"Fern\u00e1ndez Mart\u00ednez NJ, Peri\u00f1\u00e1n-Pascual C (2020) Knowledge-based rules for the extraction of complex, fine-grained locative references from tweets. RAEL Rev Electron Ling\u00fc\u00edst Apl 19:136\u2013163","journal-title":"RAEL Rev Electron Ling\u00fc\u00edst Apl"},{"key":"482_CR11","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1145\/1998076.1998140","volume-title":"Proceedings of the 11th annual international ACM\/IEEE joint conference on digital libraries","author":"N Freire","year":"2011","unstructured":"Freire N, Borbinha J, Calado P, Martins B (2011) A metadata geoparsing system for place name recognition and resolution in metadata records. Proceedings of the 11th annual international ACM\/IEEE joint conference on digital libraries. ACM, Ottawa, Ontario, Canada, pp 339\u2013348"},{"key":"482_CR12","first-page":"1","volume-title":"Language processing and intelligent information systems","author":"MB Habib","year":"2013","unstructured":"Habib MB, van Keulen M (2013) A hybrid approach for robust multilingual toponym extraction and disambiguation. In: K\u0142opotek MA, Koronacki J, Marciniak M et al (eds) Language processing and intelligent information systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 1\u201315"},{"key":"482_CR13","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1080\/13658816.2021.1947507","volume":"36","author":"X Hu","year":"2022","unstructured":"Hu X, Al-Olimat HS, Kersten J et al (2022a) GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules. Int J Geogr Inf Sci 36:310\u2013337. https:\/\/doi.org\/10.1080\/13658816.2021.1947507","journal-title":"Int J Geogr Inf Sci"},{"key":"482_CR14","doi-asserted-by":"publisher","first-page":"16259","DOI":"10.1109\/JIOT.2022.3150967","volume":"9","author":"X Hu","year":"2022","unstructured":"Hu X, Zhou Z, Sun Y et al (2022b) GazPNE2: a general place name extractor for microblogs fusing gazetteers and pretrained transformer models. IEEE Internet Things J 9:16259\u201316271. https:\/\/doi.org\/10.1109\/JIOT.2022.3150967","journal-title":"IEEE Internet Things J"},{"key":"482_CR15","doi-asserted-by":"crossref","unstructured":"Ji Z, Sun A, Cong G, Han J (2016) Joint recognition and linking of fine-grained locations from tweets. In: Proceedings of the 25th international conference on world wide web. pp 1271\u20131281","DOI":"10.1145\/2872427.2883067"},{"key":"482_CR16","doi-asserted-by":"crossref","unstructured":"Jiang H, Zhang D, Cao T, et al (2021) Named entity recognition with small strongly labeled and large weakly labeled data. In: Zong C, Xia F, Li W, Navigli R (eds) Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, pp 1775\u20131789","DOI":"10.18653\/v1\/2021.acl-long.140"},{"key":"482_CR17","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.ijdrr.2018.10.021","volume":"33","author":"A Kumar","year":"2019","unstructured":"Kumar A, Singh JP (2019) Location reference identification from tweets during emergencies: a deep learning approach. Int J Disaster Risk Reduct 33:365\u2013375. https:\/\/doi.org\/10.1016\/j.ijdrr.2018.10.021","journal-title":"Int J Disaster Risk Reduct"},{"key":"482_CR18","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s10707-022-00474-1","volume":"27","author":"X Lei","year":"2023","unstructured":"Lei X, Song W, Fan R et al (2023) Semi-supervised geological disasters named entity recognition using few labeled data. GeoInformatica 27:263\u2013288","journal-title":"GeoInformatica"},{"key":"482_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105460","volume":"116","author":"W Li","year":"2022","unstructured":"Li W, Du Y, Li X et al (2022) Ud_bbc: named entity recognition in social network combined BERT-BiLSTM-CRF with active learning. Eng Appl Artif Intell 116:105460. https:\/\/doi.org\/10.1016\/j.engappai.2022.105460","journal-title":"Eng Appl Artif Intell"},{"key":"482_CR20","unstructured":"Li F, Wang H, Kong H, et al (2023) Research on Chinese fine-grained geographic entity recognition model based on joint lexicon enhancement. J Geo-Inf Sci 1106\u20131120"},{"key":"482_CR21","doi-asserted-by":"crossref","unstructured":"Liang C, Yu Y, Jiang H, et al (2020) BOND: BERT-assisted open-domain named entity recognition with distant supervision. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. pp 1054\u20131064","DOI":"10.1145\/3394486.3403149"},{"key":"482_CR22","doi-asserted-by":"crossref","unstructured":"Lieberman MD, Samet H (2012) Adaptive context features for toponym resolution in streaming news. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval. Association for Computing Machinery, New York, NY, USA, pp 731\u2013740","DOI":"10.1145\/2348283.2348381"},{"key":"482_CR23","doi-asserted-by":"publisher","first-page":"2433","DOI":"10.1007\/s11063-021-10737-x","volume":"54","author":"M Liu","year":"2022","unstructured":"Liu M, Tu Z, Zhang T et al (2022) LTP: a new active learning strategy for CRF-based named entity recognition. Neural Process Lett 54:2433\u20132454. https:\/\/doi.org\/10.1007\/s11063-021-10737-x","journal-title":"Neural Process Lett"},{"key":"482_CR24","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s10109-022-00375-9","volume":"24","author":"K Ma","year":"2022","unstructured":"Ma K, Tan Y, Xie Z et al (2022) Chinese toponym recognition with variant neural structures from social media messages based on BERT methods. J Geogr Syst 24:143\u2013169. https:\/\/doi.org\/10.1007\/s10109-022-00375-9","journal-title":"J Geogr Syst"},{"key":"482_CR25","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-981-10-0515-2_9","volume-title":"Computational linguistics","author":"S Malmasi","year":"2016","unstructured":"Malmasi S, Dras M (2016) Location mention detection in tweets and microblogs. Computational linguistics. Springer, Singapore, pp 123\u2013134"},{"key":"482_CR26","doi-asserted-by":"publisher","DOI":"10.14188\/j.1671-8844.2020-05-012","author":"B Mao","year":"2020","unstructured":"Mao B, Teng W (2020) Complex Chinese place name recognition based on conditional random field and rule improvement. Eng J Wuhan Univ. https:\/\/doi.org\/10.14188\/j.1671-8844.2020-05-012","journal-title":"Eng J Wuhan Univ"},{"key":"482_CR27","doi-asserted-by":"crossref","unstructured":"Milleville K, Verstockt S, van de Weghe N (2020) Improving toponym recognition accuracy of historical topographic maps. In: Automatic vectorisation of historical maps: international workshop organized by the ICA commission on cartographic heritage into the digital. Budapest \u2013 13 March, 2020. Department of Cartography and Geoinformatics ELTE, pp 65\u201374","DOI":"10.21862\/avhm2020.08"},{"key":"482_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0244317","volume":"16","author":"S Milusheva","year":"2021","unstructured":"Milusheva S, Marty R, Bedoya G et al (2021) Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning. PLoS ONE 16:e0244317. https:\/\/doi.org\/10.1371\/journal.pone.0244317","journal-title":"PLoS ONE"},{"key":"482_CR29","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2019","unstructured":"Miyato T, Maeda S-I, Koyama M, Ishii S (2019) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 41:1979\u20131993. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858821","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"482_CR30","unstructured":"Paradesi S (2011) Geotagging Tweets Using Their Content. In: Proceedings of the Twenty-fourth international Florida artificial intelligence research society conference (FLAIRS 2011). Palm Beach, Florida, pp 355\u2013356"},{"key":"482_CR31","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1111\/tgis.12902","volume":"26","author":"Q Qiu","year":"2022","unstructured":"Qiu Q, Xie Z, Wang S et al (2022) ChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network. Trans GIS 26:1256\u20131279. https:\/\/doi.org\/10.1111\/tgis.12902","journal-title":"Trans GIS"},{"key":"482_CR32","doi-asserted-by":"crossref","unstructured":"R\u00f6der M, Both A, Hinneburg A (2015) Exploring the Space of topic coherence measures. In: Proceedings of the eighth ACM international conference on web search and data mining. Association for Computing Machinery, New York, NY, USA, pp 399\u2013408","DOI":"10.1145\/2684822.2685324"},{"key":"482_CR33","unstructured":"Settles B (2009) Active Learning Literature Survey. University of Wisconsin-Madison Department of Computer Sciences"},{"key":"482_CR34","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s10479-017-2522-3","volume":"283","author":"JP Singh","year":"2019","unstructured":"Singh JP, Dwivedi YK, Rana NP et al (2019) Event classification and location prediction from tweets during disasters. Ann Oper Res 283:737\u2013757. https:\/\/doi.org\/10.1007\/s10479-017-2522-3","journal-title":"Ann Oper Res"},{"key":"482_CR35","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/info13010026","volume":"13","author":"N Suat-Rojas","year":"2022","unstructured":"Suat-Rojas N, Gutierrez-Osorio C, Pedraza C (2022) Extraction and analysis of social networks data to detect traffic accidents. Information 13:26. https:\/\/doi.org\/10.3390\/info13010026","journal-title":"Information"},{"key":"482_CR36","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3390\/ijgi11120598","volume":"11","author":"L Tao","year":"2022","unstructured":"Tao L, Xie Z, Xu D et al (2022) Geographic named entity recognition by employing natural language processing and an improved BERT model. ISPRS Int J Geo-Inf 11:598. https:\/\/doi.org\/10.3390\/ijgi11120598","journal-title":"ISPRS Int J Geo-Inf"},{"key":"482_CR37","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Proceedings of the 31st international conference on neural information processing systems. Curran Associates Inc., Red Hook, NY, USA, pp 1195\u20131204"},{"key":"482_CR38","unstructured":"Tateosian L, Guenter R, Yang Y-P, Ristaino J (2017) Tracking 19th century late blight from archival documents using text analytics and geoparsing. In: Free and open source software for geospatial (FOSS4G) conference proceedings. Boston, MA, USA"},{"key":"482_CR39","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi7060217","volume":"7","author":"S Wang","year":"2018","unstructured":"Wang S, Zhang X, Ye P, Du M (2018) Deep belief networks based toponym recognition for Chinese text. ISPRS Int J Geo-Inf 7:217. https:\/\/doi.org\/10.3390\/ijgi7060217","journal-title":"ISPRS Int J Geo-Inf"},{"key":"482_CR40","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1111\/tgis.12627","volume":"24","author":"J Wang","year":"2020","unstructured":"Wang J, Hu Y, Joseph K (2020) Neurotpr: a neuro-net toponym recognition model for extracting locations from social media messages. Trans GIS 24:719\u2013735. https:\/\/doi.org\/10.1111\/tgis.12627","journal-title":"Trans GIS"},{"key":"482_CR41","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.dss.2018.04.005","volume":"111","author":"D Wu","year":"2018","unstructured":"Wu D, Cui Y (2018) Disaster early warning and damage assessment analysis using social media data and geo-location information. Decis Support Syst 111:48\u201359. https:\/\/doi.org\/10.1016\/j.dss.2018.04.005","journal-title":"Decis Support Syst"},{"key":"482_CR42","first-page":"9","volume":"12","author":"X Zhang","year":"2010","unstructured":"Zhang X, Lv G, Li B, Chen W (2010) Rule-based approach to semantic resolution of Chinese addresses. J Geo-Inf Sci 12:9\u201316","journal-title":"J Geo-Inf Sci"},{"key":"482_CR43","unstructured":"Zhang Z, Sabuncu MR (2018) Generalized cross entropy loss for training deep neural networks with noisy labels. In: Proceedings of the 32nd international conference on neural information processing systems. Curran Associates Inc., Red Hook, NY, USA, pp 8792\u20138802"},{"key":"482_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s10109-024-00441-4","author":"Y Zhao","year":"2024","unstructured":"Zhao Y, Zhang D, Jiang L et al (2024) EIBC: a deep learning framework for Chinese toponym recognition with multiple layers. J Geogr Syst. https:\/\/doi.org\/10.1007\/s10109-024-00441-4","journal-title":"J Geogr Syst"},{"key":"482_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107214","volume":"228","author":"F Zhou","year":"2021","unstructured":"Zhou F, Dai Y, Gao Q et al (2021a) Self-supervised human mobility learning for next location prediction and trajectory classification. Knowl-Based Syst 228:107214. https:\/\/doi.org\/10.1016\/j.knosys.2021.107214","journal-title":"Knowl-Based Syst"},{"key":"482_CR46","doi-asserted-by":"publisher","DOI":"10.1145\/3462331","author":"F Zhou","year":"2021","unstructured":"Zhou F, Wang P, Xu X et al (2021b) Contrastive trajectory learning for tour recommendation. ACM Trans Intell Syst Technol. https:\/\/doi.org\/10.1145\/3462331","journal-title":"ACM Trans Intell Syst Technol"}],"container-title":["Journal of Geographical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10109-025-00482-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10109-025-00482-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10109-025-00482-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T05:15:00Z","timestamp":1765775700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10109-025-00482-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["482"],"URL":"https:\/\/doi.org\/10.1007\/s10109-025-00482-3","relation":{},"ISSN":["1435-5930","1435-5949"],"issn-type":[{"type":"print","value":"1435-5930"},{"type":"electronic","value":"1435-5949"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"1 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}