{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:55:26Z","timestamp":1780091726357,"version":"3.54.0"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T00:00:00Z","timestamp":1762041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Young Talent Project of Gansu Provincial Organization Department","award":["2025QNGR14"],"award-info":[{"award-number":["2025QNGR14"]}]},{"name":"Key Talent Project of Gansu Provincial Organization Department","award":["2025RCXM012"],"award-info":[{"award-number":["2025RCXM012"]}]},{"name":"Gansu Youth Science and Technology Fund","award":["24JRRA275"],"award-info":[{"award-number":["24JRRA275"]}]},{"name":"Joint Innovation Fund Project of Lanzhou Jiaotong University and Corresponding Supporting University","award":["LH 2024019"],"award-info":[{"award-number":["LH 2024019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["42271454"],"award-info":[{"award-number":["42271454"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Project of Gansu Provincial Joint Research Fund","award":["24JRRA848"],"award-info":[{"award-number":["24JRRA848"]}]},{"name":"Lanzhou City Science and Technology Plan Project","award":["2024-3-68"],"award-info":[{"award-number":["2024-3-68"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Geographic Named Entity Recognition (Geo-NER) is a crucial task for extracting geography-related entities from unstructured text, and it plays an essential role in geographic information extraction and spatial semantic understanding. Traditional approaches typically treat Geo-NER as a sequence labeling problem, where each token is assigned a single label. However, this formulation struggles to handle nested entities effectively. To overcome this limitation, we propose Geo-MRC, an improved model based on a Machine Reading Comprehension (MRC) framework that reformulates Geo-NER as a question-answering task. The model identifies entities by predicting their start positions, end positions, and lengths, enabling precise detection of overlapping and nested entities. Specifically, it constructs a unified input sequence by concatenating a type-specific question (e.g., \u201cWhat are the location names in the text?\u201d) with the context. This sequence is encoded using BERT, followed by feature extraction and fusion through Gated Recurrent Units (GRU) and multi-scale 1D convolutions, which improve the model\u2019s sensitivity to both multi-level semantics and local contextual information. Finally, a feed-forward neural network (FFN) predicts whether each token corresponds to the start or end of an entity and estimates the span length, allowing for dynamic inference of entity boundaries. Experimental results on multiple public datasets demonstrate that Geo-MRC consistently outperforms strong baselines, with particularly significant gains on datasets containing nested entities.<\/jats:p>","DOI":"10.3390\/ijgi14110431","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T16:18:42Z","timestamp":1762186722000},"page":"431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Geo-MRC: Dynamic Boundary Inference in Machine Reading Comprehension for Nested Geographic Named Entity Recognition"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuting","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingzhong","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0202-0032","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Du","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Hao","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102537","DOI":"10.1016\/j.ipm.2021.102537","article-title":"LearningToAdapt with word embeddings: Domain adaptation of named entity recognition systems","volume":"58","author":"Nozza","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1080\/13658816.2022.2133125","article-title":"Transformer based named entity recognition for place name extraction from unstructured text","volume":"37","author":"Berragan","year":"2023","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101821","DOI":"10.1016\/j.compenvurbsys.2022.101821","article-title":"Deep learning method for Chinese multisource point of interest matching","volume":"96","author":"Li","year":"2022","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s41651-025-00232-5","article-title":"Embedding-Based Multilingual Semantic Search for Geo-Textual Data in Urban Studies","volume":"9","author":"Dunkel","year":"2025","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e1500779","DOI":"10.1126\/sciadv.1500779","article-title":"Rapid assessment of disaster damage using social media activity","volume":"2","author":"Kryvasheyeu","year":"2016","journal-title":"Sci. Adv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s41651-022-00121-1","article-title":"Context Optimized and Spatial Aware Dummy Locations Generation Framework for Location Privacy","volume":"6","author":"Tadakaluru","year":"2022","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104419","DOI":"10.1016\/j.ijdrr.2024.104419","article-title":"The characteristics and functionalities of citizen-led disaster response through social media: A case study of the #HenanFloodsRelief on Sina Weibo","volume":"106","author":"Li","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s41651-024-00184-2","article-title":"Towards responsible urban geospatial AI: Insights from the white and grey literatures","volume":"8","author":"Marasinghe","year":"2024","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tao, L., Xie, Z., Xu, D., Ma, K., Qiu, Q., Pan, S., and Huang, B. (2022). Geographic named entity recognition by employing natural language processing and an improved BERT model. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11120598"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, P., Zhu, Q., Liu, J., Liu, T., Du, P., Liu, S., and Zhang, Y. (2025). A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses. ISPRS Int. J. Geo-Inf., 14.","DOI":"10.3390\/ijgi14060227"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1007\/s10579-019-09475-3","article-title":"A pragmatic guide to geoparsing evaluation","volume":"54","author":"Gritta","year":"2020","journal-title":"Lang. Resour. Eval."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1075\/li.30.1.03nad","article-title":"A survey of named entity recognition and classification","volume":"30","author":"Nadeau","year":"2007","journal-title":"Lingvist. Investig."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1093\/bioinformatics\/btx761","article-title":"An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition","volume":"34","author":"Luo","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","article-title":"A survey on deep learning for named entity recognition","volume":"34","author":"Li","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","first-page":"23","article-title":"Application of pre-training models in named entity recognition","volume":"Volume 1","author":"Wang","year":"2020","journal-title":"Proceedings of the 2020 12th International Conference on Intelligent Human-Machine Systems and Cybernetics"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., and Li, J. (2020). A unified MRC framework for named entity recognition. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.519"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, J., Ji, B., Li, S., Ma, J., Liu, H., and Xu, H. (2022). S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition. Sensors, 22.","DOI":"10.3390\/s22082852"},{"key":"ref_18","unstructured":"Bao, X., Tian, M., Zha, Z., and Qin, B. (2023, January 21\u201325). MPMRC-MNER: Unified MRC Framework for MNER. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, UK."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1145\/1328964.1328989","article-title":"Toponym resolution in text: Annotation, evaluation and applications of spatial grounding","volume":"Volume 41","author":"Leidner","year":"2007","journal-title":"ACM SIGIR Forum"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1002\/asi.21090","article-title":"NERA: Named entity recognition for Arabic","volume":"60","author":"Shaalan","year":"2009","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_21","first-page":"17","article-title":"Recognition of chinese organization names and abbreviations","volume":"21","author":"Shen","year":"2007","journal-title":"J. Chin. Inf. Process."},{"key":"ref_22","unstructured":"Lafferty, J.D., McCallum, A., and Pereira, F. (2001, January 21\u201326). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5121\/ijnlc.2012.1402","article-title":"Named entity recognition using Hidden Markov Model (HMM)","volume":"1","author":"Morwal","year":"2012","journal-title":"Int. J. Nat. Lang. Comput. (IJNLC)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016). Neural architectures for named entity recognition. arXiv.","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s12145-019-00390-3","article-title":"BiLSTM-CRF for geological named entity recognition from the geoscience literature","volume":"12","author":"Qiu","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_26","unstructured":"Song, C.H., Lawrie, D., Finin, T., and Mayfield, T. (2020). Improving neural named entity recognition with gazetteers. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1080\/13658816.2021.1947507","article-title":"GazPNE: Annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules","volume":"36","author":"Hu","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s11063-022-10933-3","article-title":"A multi-task BERT-BiLSTM-AM-CRF strategy for Chinese named entity recognition","volume":"55","author":"Tang","year":"2023","journal-title":"Neural Process. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Ma, Z., Gao, L., and Xu, Y. (2020). An ERNIE-based joint model for Chinese named entity recognition. Appl. Sci., 10.","DOI":"10.3390\/app10165711"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cui, L., Wu, Y., Liu, J., Yang, S., and Zhang, Y. (2021). Template-based named entity recognition using BART. arXiv.","DOI":"10.18653\/v1\/2021.findings-acl.161"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3339","DOI":"10.1007\/s11063-021-10547-1","article-title":"Named entity recognition by using XLNet-BiLSTM-CRF","volume":"53","author":"Yan","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, W., Meng, J., Wan, J., Zhang, C., Zhang, J., Wang, Y., Xu, L., and Li, F. (2023). ChineseCTRE: A model for geographical named entity recognition and correction based on deep neural networks and the BERT model. ISPRS Int. J. Geo. Inf., 12.","DOI":"10.3390\/ijgi12100394"},{"key":"ref_33","unstructured":"Nayak, T., and Ng, H. (2020, January 7\u201312). Effective modeling of encoder-decoder architecture for joint entity and relation extraction. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence 2020, New York, NY, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., and Qiu, X. (2021). A unified generative framework for various NER subtasks. arXiv.","DOI":"10.18653\/v1\/2021.acl-long.451"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s12145-022-00775-x","article-title":"Few-shot learning for name entity recognition in geological text based on GeoBERT","volume":"15","author":"Liu","year":"2022","journal-title":"Earth Sci. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.1007\/s12145-023-01112-6","article-title":"CnGeoPLM: Contextual knowledge selection and embedding with pretrained language representation model for the geoscience domain","volume":"16","author":"Ma","year":"2023","journal-title":"Earth Sci. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7393","DOI":"10.1007\/s11063-023-11266-5","article-title":"FinBERT\u2013MRC: Financial named entity recognition using BERT under the machine reading comprehension paradigm","volume":"55","author":"Zhang","year":"2023","journal-title":"Neural Process. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sun, C., Yang, Z., Wang, L., Zhang, Y., Lin, H., and Wang, J. (2021). Biomedical named entity recognition using BERT in the machine reading comprehension framework. J. Biomed. Inform., 118.","DOI":"10.1016\/j.jbi.2021.103799"},{"key":"ref_39","first-page":"118","article-title":"Judicial nested named entity recognition method with MRC framework","volume":"4","author":"Zhang","year":"2023","journal-title":"Int. J. Cogn. Comput. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s13321-024-00874-5","article-title":"Application of machine reading comprehension techniques for named entity recognition in materials science","volume":"16","author":"Huang","year":"2024","journal-title":"J. Cheminform."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shrimal, A., Jain, A., Mehta, K., and Yenigalla, P. (2022). NER-MQMRC: Formulating named entity recognition as multi question machine reading comprehension. arXiv.","DOI":"10.18653\/v1\/2022.naacl-industry.26"},{"key":"ref_42","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language under-standing. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gao, Y., Xiong, Y., Wang, S., and Wang, H. (2022). GeoBERT: Pre-training geospatial representation learning on point-of-interest. Appl. Sci., 12.","DOI":"10.3390\/app122412942"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, Z., Kim, J., Chiang, Y.Y., and Chen, M. (2022, January 7\u201311). SpaBERT: A pretrained language model from geographic data for geo-entity representation. Proceedings of the Findings of the Association for Computational Linguistics: EMNLP, Miami, FL, USA.","DOI":"10.18653\/v1\/2022.findings-emnlp.200"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1007\/s10462-025-11162-5","article-title":"BERT applications in natural language processing: A review","volume":"58","author":"Gardazi","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, P., Luo, A., Liu, J., Wang, Y., Zhu, J., Deng, Y., and Zhang, J. (2020). Bidirectional gated recurrent unit neural network for Chinese address element segmentation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9110635"},{"key":"ref_48","unstructured":"Jiang, X., He, K., He, J., and Yan, G. (2021). A new entity extraction method based on machine reading comprehension. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.patrec.2023.06.011","article-title":"GFMRC: A machine reading comprehension model for named entity recognition","volume":"172","author":"Fei","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"ref_50","unstructured":"Zhang, Y., Wang, J., Zhu, X., Sakai, T., and Yamana, H. (2023). NER-to-MRC: Named-entity recognition completely solving as machine reading comprehension. arXiv."},{"key":"ref_51","unstructured":"Du, X., Zhao, H., Xing, D., Jia, Y., and Zan, H. (2024). MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training. arXiv."},{"key":"ref_52","unstructured":"Aryabumi, V., Dang, J., Talupuru, D., Dash, S., Cairuz, D., Lin, H., Venkitesh, B., Smith, M., Campos, J.A., and Tan, Y.C. (2024). Aya 23: Open weight releases to further multilingual progress. arXiv."},{"key":"ref_53","unstructured":"Team, G., Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ram\u00e9, A., and Rivi\u00e8re, M. (2025). Gemma 3 technical report. arXiv."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/11\/431\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T05:22:37Z","timestamp":1762233757000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/11\/431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,2]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["ijgi14110431"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14110431","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,2]]}}}