DePNR: A DeBERTa-based deep learning model with complete position embedding for place name recognition from geographical literature
Weirong Li
College of Environment and Resources, Guangxi Normal University, Guilin, China
Guangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, Guangxi Normal University, Guilin, China
Search for more papers by this authorCorresponding Author
Kai Sun
GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, New York, USA
Correspondence
Kai Sun, GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USA.
Email: [email protected]
Yunqiang Zhu, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Email: [email protected]
Search for more papers by this authorShu Wang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorCorresponding Author
Yunqiang Zhu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
Correspondence
Kai Sun, GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USA.
Email: [email protected]
Yunqiang Zhu, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Email: [email protected]
Search for more papers by this authorXiaoliang Dai
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorLei Hu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorWeirong Li
College of Environment and Resources, Guangxi Normal University, Guilin, China
Guangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, Guangxi Normal University, Guilin, China
Search for more papers by this authorCorresponding Author
Kai Sun
GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, New York, USA
Correspondence
Kai Sun, GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USA.
Email: [email protected]
Yunqiang Zhu, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Email: [email protected]
Search for more papers by this authorShu Wang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorCorresponding Author
Yunqiang Zhu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
Correspondence
Kai Sun, GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USA.
Email: [email protected]
Yunqiang Zhu, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Email: [email protected]
Search for more papers by this authorXiaoliang Dai
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorLei Hu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorAbstract
Place names play an important role in linking physical places to human perception and are highly frequently used in the daily lives of people to refer to places in natural language. However, many place names may not be recorded in typical gazetteers due to their new establishment, colloquial nature, and different concerns. These unrecorded toponyms are often discussed in geographical literature; thus, it is necessary to automatically identify them from geographical literature and update existing gazetteers using computational approaches. Currently, the most advanced approaches are deep learning-based models. However, existing models used only partial position information rather than complete position information of words in a sentence, which limits their performance in recognizing toponyms. To this end, we develop DePNR, a DeBERTa-based deep learning model with complete position embedding for place name recognition from geographical literature. We train DePNR on two datasets and test it on a real dataset from geographical literature to evaluate its performance. The results show that DePNR achieves an F-score of 0.8282, outperforming previous approaches, and can recognize new toponyms from literature text, potentially enriching existing gazetteers.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available in DePNR at https://github.com/liwr-igsnrr/DePNR.
REFERENCES
- Acheson, E., De Sabbata, S., & Purves, R. S. (2017). A quantitative analysis of global gazetteers: Patterns of coverage for common feature types. Computers, Environment and Urban Systems, 64, 309–320. https://doi.org/10.1016/j.compenvurbsys.2017.03.007
- Acheson, E., & Purves, R. S. (2021). Extracting and modeling geographic information from scientific articles. PLoS One, 16(1), e0244918. https://doi.org/10.1371/journal.pone.0244918
- Aktas, Y. D., Ioannou, I., Altamirano, H., Reeslev, M., D'Ayala, D., May, N., & Canales, M. (2018). Surface and passive/active air mould sampling: A testing exercise in a North London housing estate. Science of the Total Environment, 643, 1631–1643. https://doi.org/10.1016/j.scitotenv.2018.06.311
- Alex, B., Byrne, K., Grover, C., & Tobin, R. (2015). Adapting the Edinburgh geoparser for historical georeferencing. International Journal of Humanities and Arts Computing, 9(1), 15–35. https://doi.org/10.3366/ijhac.2015.0136
- Al-Olimat, H., Thirunarayan, K., Shalin, V., & Sheth, A. (2018). Location name extraction from targeted text streams using gazetteer-based statistical language models. In 27th International Conference on Computational Linguistics (pp. 1986–1997, Santa Fe, NM). Association for Computational Linguistics.
- Annad, O., Goria, S., & Bendaoud, A. (2021). Multidimensional analysis of geosciences literature for knowledge discovery. In ICGDA '21: Proceedings of the 2021 4th International Conference on Geoinformatics and Data Analysis, Marseille, France (pp. 20–29).
10.1145/3465222.3465223 Google Scholar
- Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., & Werneck, R. F. (2016). Route planning in transportation networks. In L. Kliemann & P. Sanders (Eds.), Algorithm Engineering, LNCS 9220 (pp. 19–80). Springer.
10.1007/978-3-319-49487-6_2 Google Scholar
- Cao, L., Tao, J., & Chen, B. (2018). Implementation of personalized scenic spots route recommendation system. In 2018 13th International Conference on Computer Science & Education (ICCSE), Colombo, Sri Lanka (pp. 1–6).
10.1109/ICCSE.2018.8468845 Google Scholar
- Chukwu, M., Huang, X., Wang, S., Li, X., & Wei, H. (2024). Urban park accessibility assessment using human mobility data: A systematic review. Annals of GIS, 30(2), 1–18. https://doi.org/10.1080/19475683.2024.2341700
- Clasper, L. T. (2017). Exploring vernacular perceptions of spatial entities: Using Twitter data and R for delimiting vague, informal neighbourhood units in Inner London, UK. Proceedings of VGI-Analytics, 9, 316–335. https://doi.org/10.1553/giscience2018_01_s316
10.1553/giscience2018_01_s316 Google Scholar
- de Andrade, F. G., de Souza Baptista, C., & Davis, C. A. (2014). Improving geographic information retrieval in spatial data infrastructures. GeoInformatica, 18(4), 793–818. https://doi.org/10.1007/s10707-014-0202-x
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv, 1–16. https://doi.org/10.48550/arXiv.1810.04805
10.48550/arXiv.1810.04805 Google Scholar
- Evans, A., & Waters, T. (2007). Mapping vernacular geography: Web-based GIS tools for capturing ‘fuzzy’ or ‘vague’ entities. International Journal of Technology, Policy and Management, 7(2), 134–150. https://doi.org/10.1504/IJTPM.2007.014547
10.1504/IJTPM.2007.014547 Google Scholar
- Falek, M. A., Pelsser, C., Julien, S., & Theoleyre, F. (2022). MUSE: Multimodal separators for efficient route planning in transportation networks. Transportation Science, 56, 265–564. https://doi.org/10.1287/trsc.2021.1104
- Fan, R., Wang, L., Yan, J., Song, W., Zhu, Y., & Chen, X. (2020). Deep learning-based named entity recognition and knowledge graph construction for geological hazards. ISPRS International Journal of Geo-Information, 9(1), 15. https://doi.org/10.3390/ijgi9010015
- Finkel, J. R., Grenager, T., & Manning, C. (2005). Incorporating non-local information into information extraction systems by Gibbs sampling. In 43rd Annual Meeting of the Association for Computational Linguistics, Ann Arbor, Michigan (pp. 363–370).
- Ganieva, G. (2020). Different views on the importance of toponyms. Journal of Foreign Languages and Linguistics, 1(1), 72–76.
- Gao, X., Zhang, A., & Sun, Z. (2020). How regional economic integration influence on urban land use efficiency? A case study of Wuhan metropolitan area, China. Land Use Policy, 90, 104329. https://doi.org/10.1016/j.landusepol.2019.104329
- Giridhar, P., Abdelzaher, T., George, J., & Kaplan, L. (2015). On quality of event localization from social network feeds. In IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), St. Louis, Missouri (pp. 75–80).
10.1109/PERCOMW.2015.7133997 Google Scholar
- Goodchild, M. F., & Hill, L. L. (2008). Introduction to digital gazetteer research. International Journal of Geographical Information Science, 22(10), 1039–1044. https://doi.org/10.1080/13658810701850497
- Gritta, M., Pilehvar, M. T., & Collier, N. (2018). Which Melbourne? Augmenting geocoding with maps. In 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia (pp. 1285–1296).
10.18653/v1/P18-1119 Google Scholar
- Gritta, M., Pilehvar, M. T., & Collier, N. (2020). A pragmatic guide to geoparsing evaluation: Toponyms, named entity recognition and pragmatics. Language Resources Evaluation, 54, 683–712. https://doi.org/10.1007/s10579-019-09475-3
- Gritta, M., Pilehvar, M. T., Limsopatham, N., & Collier, N. (2018). What's missing in geographical parsing? Language Resources and Evaluation, 52, 603–623. https://doi.org/10.1007/s10579-017-9385-8
- Guo, Y., Tong, L., & Mei, L. (2020). The effect of industrial agglomeration on green development efficiency in Northeast China since the revitalization. Journal of Cleaner Production, 258, 120584. https://doi.org/10.1016/j.jclepro.2020.120584
- He, P., Liu, X., Gao, J., & Chen, W. (2020). DeBERTa: Decoding-enhanced BERT with disentangled attention. arXiv. https://doi.org/10.48550/arXiv.2006.03654
10.48550/arXiv.2006.03654 Google Scholar
- Hu, X., al-Olimat, H. S., Kersten, J., Wiegmann, M., Klan, F., Sun, Y., & Fan, H. (2022). GazPNE: Annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules. International Journal of Geographical Information Science, 36(2), 310–337. https://doi.org/10.1080/13658816.2021.1947507
- Hu, X., Zhou, Z., Sun, Y., Kersten, J., Klan, F., Fan, H., & Wiegmann, M. (2022). GazPNE2: A general place name extractor for microblogs fusing gazetteers and pretrained transformer models. IEEE Internet of Things Journal, 9(17), 16259–16271. https://doi.org/10.1109/JIOT.2022.3150967
- Hu, Y., Mao, H., & Mckenzie, G. (2019). A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements. International Journal of Geographical Information Science, 33(4), 714–738. https://doi.org/10.1080/13658816.2018.1458986
- Huang, Z., Liang, D., Xu, P., & Xiang, B. (2020). Improve transformer models with better relative position embeddings. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online (pp. 3327–3335).
10.18653/v1/2020.findings-emnlp.298 Google Scholar
- Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv: 1508.01991, 1–10.
- Jie, Z., Xie, P., Lu, W., Ding, R., & Li, L. (2019). Better modeling of incomplete annotations for named entity recognition. In 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota (pp. 729–734).
10.18653/v1/N19-1079 Google Scholar
- Kaffes, V., Giannopoulos, G., Karagiannakis, N., & Tsakonas, N. (2019). Learning domain specific models for toponym interlinking. In 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, Illinois (pp. 504–507).
10.1145/3347146.3359339 Google Scholar
- Kang, S., Zhang, Q., Qian, Y., Ji, Z., Li, C., Cong, Z., Zhang, Y., Guo, J., du, W., Huang, J., You, Q., Panday, A. K., Rupakheti, M., Chen, D., Gustafsson, Ö., Thiemens, M. H., & Qin, D. (2019). Linking atmospheric pollution to cryospheric change in the Third Pole region: Current progress and future prospects. National Science Review, 6(4), 796–809. https://doi.org/10.1093/nsr/nwz031
- Kim, J., Vasardani, M., & Winter, S. (2017). Similarity matching for integrating spatial information extracted from place descriptions. International Journal of Geographical Information Science, 31(1), 56–80. https://doi.org/10.1080/13658816.2016.1188930
- Li, L., Ding, Z., & Huang, D. (2008). Recognizing location names from Chinese texts based on max-margin Markov network. In International Conference on Natural Language Processing and Knowledge Engineering, Beijing, China (pp. 1–7).
- Li, L., Mao, T., & Huang, D. (2005). Extracting location names from Chinese texts based on SVM and KNN. In International Conference on Natural Language Processing and Knowledge Engineering, Wuhan, China (pp. 371–375).
- Li, W., Sun, K., Zhu, Y., Ding, F., Hu, L., Dai, X., Song, J., Yang, J., Qian, L., & Wang, S. (2024). GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding. Expert Systems with Applications, 235, 121077. https://doi.org/10.1016/j.eswa.2023.121077
- Li, W., Sun, K., Zhu, Y., Song, J., Yang, J., Qian, L., & Wang, S. (2021). Analyzing the research evolution in response to COVID-19. International Journal of Geo-Information, 10(4), 237. https://doi.org/10.3390/ijgi10040237
- Lin, T., Wang, Y., Liu, X., & Qiu, X. (2022). A survey of transformers. AI Open.
10.1016/j.aiopen.2022.10.001 Google Scholar
- Lingad, J., Karimi, S., & Yin, J. (2013). Location extraction from disaster-related microblogs. In WWW'13 Companion: Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil (pp. 1017–1020).
10.1145/2487788.2488108 Google Scholar
- Liu, D., Clarke, K. C., & Chen, N. (2020). Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area. Computers, Environment and Urban Systems, 84, 101545. https://doi.org/10.1016/j.compenvurbsys.2020.101545
- Liu, X., Lu, Y., Yu, H., Ma, L., Li, X., Li, W., Zhang, H., & Bian, C. (2022). In-situ observation of storm-induced wave-supported fluid mud occurrence in the subaqueous yellow river delta. Journal of Geophysical Research: Oceans, 127(7), e2021JC018190. https://doi.org/10.1029/2021JC018190
- Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2005). Geographic information systems and science. Wiley.
- Lu, S., Tang, X., Guan, X., Qin, F., Liu, X., & Zhang, D. (2020). The assessment of forest ecological security and its determining indicators: A case study of the Yangtze River Economic Belt in China. Journal of Environmental Management, 258, 110048. https://doi.org/10.1016/j.jenvman.2019.110048
- Luo, Q., Luo, L., Zhou, Q., & Song, Y. (2019). Does China's Yangtze River Economic Belt policy impact on local ecosystem services? Science of the Total Environment, 676, 231–241. https://doi.org/10.1016/j.scitotenv.2019.04.135
- Ma, W., Cui, Y., Si, C., Liu, T., Wang, S., & Hu, G. (2020). CharBERT: Character-aware pre-trained language model. arXiv. https://doi.org/10.48550/arXiv.2011.01513
10.48550/arXiv.2011.01513 Google Scholar
- Magge, A., Weissenbacher, D., Sarker, A., Scotch, M., & Gonzalez-Hernandez, G. (2019). Bi-directional recurrent neural network models for geographic location extraction in biomedical literature. Pacific Symposium on Biocomputing, 24, 100–111. https://doi.org/10.1142/9789813279827_0010
- Middleton, S. E., Kordopatis-Zilos, G., Papadopoulos, S., & Kompatsiaris, Y. (2018). Location extraction from social media: Geoparsing, location disambiguation, and geotagging. ACM Transactions on Information Systems, 36(4), 1–27. https://doi.org/10.1145/3202662
- Miguel, W., Patricia, M. F., & Bruno, M. (2018). Ensemble named entity recognition (NER): Evaluating NER tools in the identification of place names in historical corpora. Frontiers in Digital Humanities, 5, 2. https://doi.org/10.3389/fdigh.2018.00002
10.3389/fdigh.2018.00002 Google Scholar
- Milanova, I., Silc, J., Serucnik, M., Eftimov, T., & Gjoreski, H. (2019). LOCALE: A rule-based location named-entity recognition method for Latin text. In HistoInformatics@ Tpdl, Oslo, Norway (pp. 13–20).
- Molina-Villegas, A., Muñiz-Sanchez, V., Arreola-Trapala, J., & Alcántara, F. (2021). Geographic named entity recognition and disambiguation in Mexican news using word embeddings. Expert Systems with Applications, 176, 114855. https://doi.org/10.1016/j.eswa.2021.114855
- Montello, D. R., Goodchild, M. F., Gottsegen, J., & Fohl, P. (2003). Where's downtown?: Behavioral methods for determining referents of vague spatial queries. Spatial Cognition and Computation, 3(2-3), 185–204. https://doi.org/10.1080/13875868.2003.9683761
10.1080/13875868.2003.9683761 Google Scholar
- Purves, R., & Jones, C. (2011). Geographic information retrieval. SIGSPATIAL Special, 3(2), 2–4. https://doi.org/10.1145/2047296.2047297
10.1145/2047296.2047297 Google Scholar
- Qiu, Q., Xie, Z., Wang, S., Zhu, Y., Lv, H., & Sun, K. (2022). ChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network. Transactions in GIS, 26(3), 1256–1279. https://doi.org/10.1111/tgis.12902
- Shaw, P., Uszkoreit, J., & Vaswani, A. (2018). Self-attention with relative position representations. In 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2, New Orleans, Louisiana (Short Papers).
- Tambassi, T. (2019). What a geographical entity could be. Springer Geography.
10.1007/978-3-030-16829-2_9 Google Scholar
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, Long Beach, California (pp. 1–15).
- Villette, J., & Purves, R. S. (2018). In 21th AGILE Conference on Geographic Information Science, Lund, Sweden (pp. 1–5).
- Wallgrün, J. O., Karimzadeh, M., MacEachren, A. M., & Pezanowski, S. (2018). GeoCorpora: Building a corpus to test and train microblog geoparsers. International Journal of Geographical Information Science, 32(1), 1–29. https://doi.org/10.1080/13658816.2017.1368523
- Wang, J., Hu, Y., & Joseph, K. (2020). NeuroTPR: A neuro-net toponym recognition model for extracting locations from social media messages. Transactions in GIS, 24, 719–735. https://doi.org/10.1111/tgis.12627
- Wang, L., Yao, T., Chai, C., Cuo, L., Su, F., Zhang, F., Yao, Z., Zhang, Y., Li, X., Qi, J., & Hu, Z. (2021). TP-River: Monitoring and quantifying total river runoff from the Third Pole. Bulletin of the American Meteorological Society, 102, E948–E965. https://doi.org/10.1175/BAMS-D-20-0207.1
- Wang, X., Ma, C., Zheng, H., Liu, C., Xie, P., Li, L., & Si, L. (2019). Dm_nlp at semeval-2018 task 12: A pipeline system for toponym resolution. In 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota (pp. 917–923).
- Weischedel, R., Pradhan, S., Ramshaw, L., Kaufman, J., Franchini, M., & El-Bachouti, M. (2013). OntoNotes release 5.0. Web Down. Linguistic Data Consortium.
- Xie, C., Zhang, L., & Zhong, Z. (2023). A novel method for constructing spatiotemporal knowledge graph for maritime ship activities. Electronics, 12(15), 3205. https://doi.org/10.3390/electronics12153205
- Yadav, V., Laparra, E., Wang, T. T., Surdeanu, M., & Bethard, S. (2019). University of Arizona at semeval-2019 task 12: Deep-affix named entity recognition of geolocation entities. In 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota (pp. 1319–1323).
- Yan, R., Jiang, X., & Dang, D. (2021). Named entity recognition by using XLNet-BiLSTM-CRF. Neural Processing Letters, 53(5), 3339–3356. https://doi.org/10.1007/s11063-021-10547-1
- Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., & Le, Q. V. (2019). XLNet: Generalized autoregressive pretraining for language understanding. In 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada (pp. 5753–5763).
- Yenkar, P., & Sawarkar, S. (2021). Gazetteer based unsupervised learning approach for location extraction from complaint tweets. IOP Conference Series: Materials Science and Engineering, 1049, 012009. https://doi.org/10.1088/1757-899X/1049/1/012009
10.1088/1757-899X/1049/1/012009 Google Scholar
- Yin, C., Liu, W., Yin, D., Zhai, X., Liu, K., Jing, C., & Huang, H. (2020). Rapid extraction of research areas from scientific and technological literature. Sensors and Materials, 32(12), 4489–4504. https://doi.org/10.18494/SAM.2020.3127
- Yi-Wen, L. I., Zu, Y.-X., Li, W.-H., & Hou, B. (2017). An effective method of tourist identification and scenic spot recommendation based on mobile location information. In 3rd International Conference on Computer Science and Mechanical Automation, Wuhan, China (pp. 1–10). https://doi.org/10.12783/dtcse/csma2017/17332
- Yu, Y., Wang, Y., Mu, J., Li, W., Jiao, S., Wang, Z., Lv, P., & Zhu, Y. (2022). Chinese mineral named entity recognition based on BERT model. Expert Systems with Applications, 206, 117727. https://doi.org/10.1016/j.eswa.2022.117727
- Yue, D., Zhou, Y., Guo, J., Chao, Z., & Guo, X. (2022). Relationship between net primary productivity and soil water content in the Shule River Basin. Catena, 208, 105770. https://doi.org/10.1016/j.catena.2021.105770
- Zelinsky, W. (1955). Some problems in the distribution of generic terms in the place—Names of the northeastern United States. Annals of the Association of American Geographers, 45(4), 319–349. https://doi.org/10.1111/j.1467-8306.1955.tb01491.x
- Zhang, M., & Wang, J. (2022). Global flood disaster research graph analysis based on literature mining. Applied Sciences, 12(6), 3066. https://doi.org/10.3390/app12063066
- Zhang, X. (2009). Extraction and visualization of geographical names in text. In 24th International Cartographic Conference, Santiago, Chile (pp. 1–10).
- Zhang, X., Huang, Y., Zhang, C., & Ye, P. (2022). Geoscience knowledge graph (GeoKG): Development, construction and challenges. Transactions in GIS, 26(6), 2480–2494. https://doi.org/10.1111/tgis.12985
- Zhao, G., Wang, Y., Huang, B., Dong, Y., Li, S., Zhang, G., & Yu, S. (2018). Geological reconstructions of the East Asian blocks: From the breakup of Rodinia to the assembly of Pangea. Earth-Science Reviews, 186, 262–286. https://doi.org/10.1016/j.earscirev.2018.10.003
- Zhao, Q., Bai, J., Gao, Y., Zhao, H., Zhang, G., & Cui, B. (2020). Shifts in the soil bacterial community along a salinity gradient in the Yellow River Delta. Land Degradation & Development, 31(16), 2255–2267. https://doi.org/10.1002/ldr.3594
