{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:01:29Z","timestamp":1782572489590,"version":"3.54.5"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Key R&D Program of China","award":["2022YFF0711600"],"award-info":[{"award-number":["2022YFF0711600"]}]},{"name":"the Natural Science Foundation of Hubei Province of China","award":["No.2022CFB640"],"award-info":[{"award-number":["No.2022CFB640"]}]},{"name":"the Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering","award":["No. 2022SDSJ04"],"award-info":[{"award-number":["No. 2022SDSJ04"]}]},{"name":"the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["No, KF-2022-07-014"],"award-info":[{"award-number":["No, KF-2022-07-014"]}]},{"DOI":"10.13039\/501100002858","name":"the China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["No.2021M702991"],"award-info":[{"award-number":["No.2021M702991"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s12145-023-01207-0","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T20:01:53Z","timestamp":1704744113000},"page":"1867-1887","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A deep learning-based method for deep information extraction from multimodal data for geological reports to support geological knowledge graph construction"],"prefix":"10.1007","volume":"17","author":[{"given":"Yan","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qirui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liufeng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingyao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinjun","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"issue":"11","key":"1207_CR1","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274\u20132282","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1207_CR2","doi-asserted-by":"crossref","unstructured":"Bergen KJ, Johnson PA, de Hoop MV, Beroza GC (2019) Machine learning for data-driven discovery in solid Earth geoscience. Science 363(6433):eaau0323","DOI":"10.1126\/science.aau0323"},{"key":"1207_CR3","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neucom.2017.03.051","volume":"245","author":"S Bianco","year":"2017","unstructured":"Bianco S, Buzzelli M, Mazzini D, Schettini R (2017) Deep learning for logo recognition. Neurocomputing 245:23\u201330","journal-title":"Neurocomputing"},{"key":"1207_CR4","doi-asserted-by":"crossref","unstructured":"Bordes A, Weston J, Collobert R, Bengio Y (2011) Learning structured embeddings of knowledge bases. In: Twenty-fifth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v25i1.7917"},{"key":"1207_CR5","doi-asserted-by":"crossref","unstructured":"Budi I, Bressan S (2003). Association rules mining for name entity recognition. In: Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003. IEEE, pp 325\u2013328","DOI":"10.1109\/WISE.2003.1254504"},{"key":"1207_CR6","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ins.2014.01.015","volume":"275","author":"CP Chen","year":"2014","unstructured":"Chen CP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Inform Sci 275:314\u2013347","journal-title":"Inform Sci"},{"key":"1207_CR7","unstructured":"Chen J-P, Hou C-B, Wang G-W, Lv P, Zhu P-F, Zeng M, Wu W (2005) Research on text data mining in quantitative evaluation of mineral resources[J]. Comput Tech Geophys Geochem Explor (03):263\u2013266+184"},{"issue":"4","key":"1207_CR8","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1207_CR9","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"12","key":"1207_CR10","doi-asserted-by":"crossref","first-page":"e2022EA002617","DOI":"10.1029\/2022EA002617","volume":"9","author":"S Chen","year":"2022","unstructured":"Chen S, Hua W, Liu X, Deng X, Zeng X, Duan J (2022) Chinese fine-grained geological named entity recognition with rules and FLAT. Earth Space Sci 9(12):e2022EA002617","journal-title":"Earth Space Sci"},{"issue":"1","key":"1207_CR11","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1144\/SP499-2019-158","volume":"499","author":"Q Cheng","year":"2020","unstructured":"Cheng Q, Oberh\u00e4nsli R, Zhao M (2020) A new international initiative for facilitating data-driven Earth science transformation. Geol Soc London Spec Publ 499(1):225\u2013240","journal-title":"Geol Soc London Spec Publ"},{"issue":"11","key":"1207_CR12","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1080\/13658816.2022.2087224","volume":"36","author":"D Chu","year":"2022","unstructured":"Chu D, Wan B, Li H, Dong S, Fu J, Liu Y, Liu H (2022) A machine learning approach to extracting spatial information from geological texts in Chinese. Int J Geogr Inf Sci 36(11):2169\u20132193","journal-title":"Int J Geogr Inf Sci"},{"key":"1207_CR13","unstructured":"Consoli B, Santos J, Gomes D, Cordeiro F, Vieira R, Moreira V (2020) Embeddings for named entity recognition in geoscience Portuguese literature. In: Proceedings of the 12th Language Resources and Evaluation Conference, pp 4625\u20134630"},{"issue":"22","key":"1207_CR14","doi-asserted-by":"crossref","first-page":"6154","DOI":"10.1073\/pnas.1516510113","volume":"113","author":"K Deng","year":"2016","unstructured":"Deng K, Bol PK, Li KJ et al (2016) On the unsupervised analysis of domain-specific Chinese texts. Proc Natl Acad Sci USA 113(22):6154\u20136159","journal-title":"Proc Natl Acad Sci USA"},{"key":"1207_CR15","doi-asserted-by":"crossref","unstructured":"Enkhsaikhan M, Liu W, Holden EJ, Duuring P (2018) Towards geological knowledge discovery using vector-based semantic similarity. In: International Conference on Advanced Data Mining and Applications. Springer, Cham, pp 224\u2013237","DOI":"10.1007\/978-3-030-05090-0_20"},{"key":"1207_CR16","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2021.104200","volume":"135","author":"M Enkhsaikhan","year":"2021","unstructured":"Enkhsaikhan M, Holden EJ, Duuring P, Liu W (2021a) Understanding ore-forming conditions using machine reading of text. Ore Geol Rev 135:104200","journal-title":"Ore Geol Rev"},{"issue":"3","key":"1207_CR17","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s10115-020-01532-6","volume":"63","author":"M Enkhsaikhan","year":"2021","unstructured":"Enkhsaikhan M, Liu W, Holden EJ, Duuring P (2021b) Auto-labelling entities in low-resource text: a geological case study. Knowl Inf Syst 63(3):695\u2013715","journal-title":"Knowl Inf Syst"},{"issue":"1","key":"1207_CR18","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3390\/ijgi9010015","volume":"9","author":"R Fan","year":"2019","unstructured":"Fan R, Wang L, Yan J, Song W, Zhu Y, Chen X (2019) Deep learning-based named entity recognition and knowledge graph construction for geological hazards. ISPRS Int J Geo-Inf 9(1):15","journal-title":"ISPRS Int J Geo-Inf"},{"issue":"2","key":"1207_CR19","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput vis 59(2):167\u2013181","journal-title":"Int J Comput vis"},{"key":"1207_CR20","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.csl.2017.06.007","volume":"47","author":"O Ghahabi","year":"2018","unstructured":"Ghahabi O, Hernando J (2018) Restricted boltzmann machines for vector representation of speech in speaker recognition. Comput Speech Lang 47:16\u201329","journal-title":"Comput Speech Lang"},{"key":"1207_CR21","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2021.104201","volume":"137","author":"T Gillfeather-Clark","year":"2021","unstructured":"Gillfeather-Clark T, Horrocks T, Holden EJ, Wedge D (2021) A comparative study of neural network methods for first break detection using seismic refraction data over a detrital iron ore deposit. Ore Geol Rev 137:104201","journal-title":"Ore Geol Rev"},{"key":"1207_CR22","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.gexplo.2015.06.001","volume":"157","author":"AM Gonbadi","year":"2015","unstructured":"Gonbadi AM, Tabatabaei SH, Carranza EJM (2015) Supervised geochemical anomaly detection by pattern recognition. J Geochem Explor 157:81\u201391","journal-title":"J Geochem Explor"},{"key":"1207_CR23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.cosrev.2018.06.001","volume":"29","author":"A Goyal","year":"2018","unstructured":"Goyal A, Gupta V, Kumar M (2018) Recent named entity recognition and classification techniques: a systematic review. Comput Sci Rev 29:21\u201343","journal-title":"Comput Sci Rev"},{"issue":"35","key":"1207_CR24","doi-asserted-by":"crossref","first-page":"5066","DOI":"10.1007\/s11434-014-0645-3","volume":"59","author":"H Guo","year":"2014","unstructured":"Guo H, Wang L, Chen F, Liang D (2014) Scientific big data and digital earth. Chin Sci Bull 59(35):5066\u20135073","journal-title":"Chin Sci Bull"},{"key":"1207_CR25","unstructured":"Gupta S (2015) Building Web Applications with Python and Neo4j[M]. Packt Publishing Ltd"},{"key":"1207_CR26","doi-asserted-by":"crossref","unstructured":"Gupta N, Singh S, Roth D (2017) Entity linking via joint encoding of types, descriptions, and context. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, pp 2681\u20132690. https:\/\/www.aclweb.org\/anthology\/D17-1284","DOI":"10.18653\/v1\/D17-1284"},{"key":"1207_CR27","doi-asserted-by":"crossref","unstructured":"Harisinghaney A, Dixit A, Gupta S, Arora A (2014) Text and image based spam email classification using KNN, Na\u00efve Bayes and reverse DBSCAN algorithm. In: Proceedings of International Conference on Optimization, Reliabilty, and Information Technology (ICROIT). IEEE, pp 153\u2013155","DOI":"10.1109\/ICROIT.2014.6798302"},{"key":"1207_CR28","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"issue":"7","key":"1207_CR29","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"1207_CR30","doi-asserted-by":"crossref","unstructured":"Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, ... Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag 29(6):82\u201397","DOI":"10.1109\/MSP.2012.2205597"},{"key":"1207_CR31","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2019.05.005","volume":"111","author":"EJ Holden","year":"2019","unstructured":"Holden EJ, Liu W, Horrocks T, Wang R, Wedge D, Duuring P, Beardsmore T (2019) GeoDocA\u2013Fast analysis of geological content in mineral exploration reports: A text mining approach. Ore Geol Rev 111:102919","journal-title":"Ore Geol Rev"},{"key":"1207_CR32","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.cageo.2014.11.005","volume":"76","author":"L Huang","year":"2015","unstructured":"Huang L, Du Y, Chen G (2015) GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain. Comput Geosci 76:11\u201317","journal-title":"Comput Geosci"},{"issue":"15","key":"1207_CR33","doi-asserted-by":"crossref","first-page":"7708","DOI":"10.3390\/app12157708","volume":"12","author":"C Huang","year":"2022","unstructured":"Huang C, Wang Y, Yu Y, Hao Y, Liu Y, Zhao X (2022) Chinese named entity recognition of geological news based on BERT model. Appl Sci 12(15):7708","journal-title":"Appl Sci"},{"key":"1207_CR34","unstructured":"Huang S, Sun X, Wang H (2017) Addressing domain adaptation for Chinese word segmentation with global recurrent structure. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing vol. 1, pp 184\u2013193 (Volume 1: Long Papers)"},{"key":"1207_CR35","doi-asserted-by":"crossref","unstructured":"Kanezaki A (2018) Unsupervised image segmentation by backpropagation. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1543\u20131547","DOI":"10.1109\/ICASSP.2018.8462533"},{"issue":"1","key":"1207_CR36","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TITS.2010.2073466","volume":"12","author":"JF Khan","year":"2010","unstructured":"Khan JF, Bhuiyan SM, Adhami RR (2010) Image segmentation and shape analysis for road-sign detection. IEEE Trans Intell Transp Syst 12(1):83\u201396","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6266","key":"1207_CR37","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1126\/science.aab3050","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350(6266):1332\u20131338","journal-title":"Science"},{"key":"1207_CR38","doi-asserted-by":"crossref","unstructured":"Lang Y, Zheng D (2016) An improved Sobel edge detection operator. In: Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)","DOI":"10.2991\/mcei-16.2016.123"},{"issue":"7553","key":"1207_CR39","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"2","key":"1207_CR40","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10707-022-00474-1","volume":"27","author":"X Lei","year":"2023","unstructured":"Lei X, Song W, Fan R, Feng R, Wang L (2023) Semi-supervised geological disasters named entity recognition using few labeled data. GeoInformatica 27(2):263\u2013288","journal-title":"GeoInformatica"},{"issue":"12","key":"1207_CR41","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","volume":"31","author":"A Levinshtein","year":"2009","unstructured":"Levinshtein A, Stere A, Kutulakos KN, Fleet DJ, Dickinson SJ, Siddiqi K (2009) Turbopixels: Fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell 31(12):2290\u20132297","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"1207_CR42","doi-asserted-by":"crossref","first-page":"04015096","DOI":"10.1061\/(ASCE)GT.1943-5606.0001428","volume":"142","author":"XY Li","year":"2016","unstructured":"Li XY, Zhang LM, Li JH (2016) Using conditioned random field to characterize the variability of geologic profiles. J Geotech Geoenviron Eng 142(4):04015096","journal-title":"J Geotech Geoenviron Eng"},{"key":"1207_CR43","doi-asserted-by":"crossref","unstructured":"Li W, Ma K, Qiu Q, Wu L, Xie Z, Li S, Chen S (2021) Chinese word segmentation based on self\u2010learning model and geological knowledge for the geoscience domain. Earth Space Sci 8(6):e2021EA001673","DOI":"10.1029\/2021EA001673"},{"issue":"2","key":"1207_CR44","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","volume":"36","author":"A Likas","year":"2003","unstructured":"Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recog 36(2):451\u2013461","journal-title":"Pattern Recog"},{"issue":"1","key":"1207_CR45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13755-015-0009-7","volume":"3","author":"W Liu","year":"2015","unstructured":"Liu W, Chung BC, Wang R, Ng J, Morlet N (2015) A genetic algorithm enabled ensemble for unsupervised medical term extraction from clinical letters. Health Inform Sci Syst 3(1):1\u201314","journal-title":"Health Inform Sci Syst"},{"issue":"6","key":"1207_CR46","doi-asserted-by":"crossref","first-page":"669","DOI":"10.3390\/min12060669","volume":"12","author":"C Liu","year":"2022","unstructured":"Liu C, Chen J, Li S, Qin T (2022a) Construction of conceptual prospecting model based on geological big data: a case study in Songtao-Huayuan Area, Hunan Province. Minerals 12(6):669","journal-title":"Minerals"},{"issue":"2","key":"1207_CR47","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s12145-022-00775-x","volume":"15","author":"H Liu","year":"2022","unstructured":"Liu H, Qiu Q, Wu L, Li W, Wang B, Zhou Y (2022b) Few-shot learning for name entity recognition in geological text based on GeoBERT. Earth Sci Inform 15(2):979\u2013991","journal-title":"Earth Sci Inform"},{"key":"1207_CR48","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.neucom.2021.10.101","volume":"473","author":"P Liu","year":"2022","unstructured":"Liu P, Guo Y, Wang F, Li G (2022c) Chinese named entity recognition: the state of the art. Neurocomputing 473:37\u201353","journal-title":"Neurocomputing"},{"key":"1207_CR49","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1207_CR50","doi-asserted-by":"crossref","first-page":"5705","DOI":"10.1109\/ACCESS.2017.2785229","volume":"6","author":"X Luo","year":"2017","unstructured":"Luo X, Zhou W, Wang W, Zhu Y, Deng J (2017) Attention-based relation extraction with bidirectional gated recurrent unit and highway network in the analysis of geological data. IEEE Access 6:5705\u20135715","journal-title":"IEEE Access"},{"key":"1207_CR51","doi-asserted-by":"crossref","unstructured":"Lv X, Xie Z, Xu D, Jin X, Ma K, Tao L, ... Pan Y (2022) Chinese named entity recognition in the geoscience domain based on bert.\u00a0Earth Space Sci 9(3):e2021EA002166","DOI":"10.1029\/2021EA002166"},{"key":"1207_CR52","doi-asserted-by":"crossref","unstructured":"Ma X (2022) Knowledge graph construction and application in geosciences: A review. Comput Geosci: 105082","DOI":"10.1016\/j.cageo.2022.105082"},{"key":"1207_CR53","doi-asserted-by":"crossref","unstructured":"Ma K, Tian M, Tan Y, Xie X, Qiu Q (2022a) What is this article about? Generative summarization with the BERT model in the geosciences domain. Earth Sci Inform: 1\u201316","DOI":"10.1007\/s12145-021-00695-2"},{"key":"1207_CR54","doi-asserted-by":"crossref","unstructured":"Ma Y, Xie Z, Li G, Ma K, Huang Z, Qiu Q, Liu H (2022b) Text visualization for geological hazard documents via text mining and natural language processing. Earth Sci Inform: 1\u201316","DOI":"10.1007\/s12145-021-00732-0"},{"key":"1207_CR55","doi-asserted-by":"crossref","unstructured":"Ma K, Zheng S, Tian M, Qiu Q, Tan Y, Hu X, ... Xie Z (2023) CnGeoPLM: Contextual knowledge selection and embedding with pretrained language representation model for the geoscience domain. Earth Sci Inform: 1\u201318","DOI":"10.1007\/s12145-023-01112-6"},{"key":"1207_CR56","doi-asserted-by":"crossref","unstructured":"Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Linguisticae Investigationes 30(1):3\u201326. John Benjamins Publishing Company","DOI":"10.1075\/li.30.1.03nad"},{"issue":"6","key":"1207_CR57","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1130\/G38931.1","volume":"45","author":"SE Peters","year":"2017","unstructured":"Peters SE, Husson JM, Wilcots J (2017) The rise and fall of stromatolites in shallow marine environments. Geology 45(6):487","journal-title":"Geology"},{"issue":"1","key":"1207_CR58","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1146\/annurev.bioeng.2.1.315","volume":"2","author":"DL Pham","year":"2000","unstructured":"Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Ann Rev Biomed Eng 2(1):315\u2013337","journal-title":"Ann Rev Biomed Eng"},{"key":"1207_CR59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2018.08.006","volume":"121","author":"Q Qiu","year":"2018","unstructured":"Qiu Q, Xie Z, Wu L, Li W (2018a) DGeoSegmenter: A dictionary-based Chinese word segmenter for the geoscience domain. Comput Geosci 121:1\u201311","journal-title":"Comput Geosci"},{"issue":"1","key":"1207_CR60","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1139\/geomat-2018-0007","volume":"72","author":"Q Qiu","year":"2018","unstructured":"Qiu Q, Xie Z, Wu L (2018b) A cyclic self-learning Chinese word segmentation for the geoscience domain. Geomatica 72(1):16\u201326","journal-title":"Geomatica"},{"key":"1207_CR61","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.eswa.2019.02.001","volume":"125","author":"Q Qiu","year":"2019","unstructured":"Qiu Q, Xie Z, Wu L, Li W (2019a) Geoscience keyphrase extraction algorithm using enhanced word embedding. Expert Syst Appl 125:157\u2013169","journal-title":"Expert Syst Appl"},{"issue":"6","key":"1207_CR62","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1029\/2019EA000610","volume":"6","author":"Q Qiu","year":"2019","unstructured":"Qiu Q, Xie Z, Wu L, Tao L (2019b) GNER: A generative model for geological named entity recognition without labeled data using deep learning. Earth Space Sci 6(6):931\u2013946","journal-title":"Earth Space Sci"},{"key":"1207_CR63","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s12145-019-00390-3","volume":"12","author":"Q Qiu","year":"2019","unstructured":"Qiu Q, Xie Z, Wu L, Tao L, Li W (2019c) BiLSTM-CRF for geological named entity recognition from the geoscience literature. Earth Sci Inform 12:565\u2013579","journal-title":"Earth Sci Inform"},{"issue":"4","key":"1207_CR64","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1007\/s12145-020-00527-9","volume":"13","author":"Q Qiu","year":"2020","unstructured":"Qiu Q, Xie Z, Wu L et al (2020a) Automatic spatiotemporal and semantic information extraction from unstructured geoscience reports using text mining techniques[J]. Earth Sci Inform 13(4):1393\u20131410","journal-title":"Earth Sci Inform"},{"key":"1207_CR65","doi-asserted-by":"crossref","unstructured":"Qiu Q, Xie Z, Wu L, et al (2020b) Dictionary\u2010based automated information extraction from geological documents using a deep learning algorithm[J]. Earth Space Sci 7(3)","DOI":"10.1029\/2019EA000993"},{"key":"1207_CR66","doi-asserted-by":"crossref","unstructured":"Qiu Q, Xie Z, Xie H, Wang B (2021) GKEEP: an enhanced graph\u2010based keyword extractor with error\u2010feedback propagation for geoscience reports.\u00a0Earth Space Sci 8(5):e2020EA001602","DOI":"10.1029\/2020EA001602"},{"key":"1207_CR67","doi-asserted-by":"crossref","unstructured":"Qiu Q, Wang B, Ma K, Xie Z (2022) Geological profile-text information association model of mineral exploration reports for fast analysis of geological content.\u00a0Ore Geol Rev: 105278","DOI":"10.1016\/j.oregeorev.2022.105278"},{"key":"1207_CR68","doi-asserted-by":"crossref","unstructured":"Qiu Q, Ma K, Lv H, Tao L, Xie Z (2023a) Construction and application of a knowledge graph for iron deposits using text mining analytics and a deep learning algorithm. Math Geosci: 1\u201334","DOI":"10.1007\/s11004-023-10050-4"},{"key":"1207_CR69","doi-asserted-by":"crossref","unstructured":"Qiu Q, Tian M, Ma K, Tan YJ, Tao L, Xie Z (2023b) A question answering system based on mineral exploration ontology generation: A deep learning methodology. Ore Geol Rev: 105294","DOI":"10.1016\/j.oregeorev.2023.105294"},{"key":"1207_CR70","doi-asserted-by":"crossref","unstructured":"Qiu Q, Xie Z, Ma K et al (2023c) BERTCWS: unsupervised multi-granular Chinese word segmentation based on a BERT method for the geoscience domain[J]. Ann GIS: 1\u201313","DOI":"10.1080\/19475683.2023.2186487"},{"issue":"7743","key":"1207_CR71","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","volume":"566","author":"M Reichstein","year":"2019","unstructured":"Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N (2019) Deep learning and process understanding for data-driven earth system science. Nature 566(7743):195\u2013204","journal-title":"Nature"},{"issue":"2","key":"1207_CR72","doi-asserted-by":"crossref","first-page":"62","DOI":"10.4018\/ijrsda.2014070105","volume":"1","author":"P Roy","year":"2014","unstructured":"Roy P, Goswami S, Chakraborty S, Azar AT, Dey N (2014) Image segmentation using rough set theory: a review. Int J Rough Sets Data Anal (IJRSDA) 1(2):62\u201374","journal-title":"Int J Rough Sets Data Anal (IJRSDA)"},{"issue":"3","key":"1207_CR73","first-page":"1","volume":"32","author":"R Santos","year":"2017","unstructured":"Santos R, Murrietaflores P, Calado P, Martins B (2017) Toponym matching through deep neural networks. Int J Geogr Inf Sci 32(3):1\u201325","journal-title":"Int J Geogr Inf Sci"},{"key":"1207_CR74","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"1207_CR75","doi-asserted-by":"crossref","first-page":"52286","DOI":"10.1109\/ACCESS.2018.2870203","volume":"6","author":"L Shi","year":"2018","unstructured":"Shi L, Jianping C, Jie X (2018) Prospecting information extraction by text mining based on convolutional neural networks\u2013a case study of the Lala copper deposit, China. IEEE Access 6:52286\u201352297","journal-title":"IEEE Access"},{"issue":"3","key":"1207_CR76","first-page":"143","volume":"1","author":"N Sobhana","year":"2010","unstructured":"Sobhana N, Mitra P, Ghosh SK (2010) Conditional random field based named entity recognition in geological text. Int J Comput Appl 1(3):143\u2013147","journal-title":"Int J Comput Appl"},{"key":"1207_CR77","doi-asserted-by":"crossref","unstructured":"Sobhana NV, Ghosh SK, Mitra P (2012) Entity relation extraction from geological text using conditional random fields and subsequence kernels. In: 2012 Annual IEEE India Conference (INDICON). IEEE, pp 832\u2013840","DOI":"10.1109\/INDCON.2012.6420733"},{"key":"1207_CR78","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201914 MIT Press, pp 3104\u20133112. http:\/\/dl.acm.org\/citation.cfm?id=2969033.2969173"},{"key":"1207_CR79","doi-asserted-by":"crossref","unstructured":"Tang M, Gorelick L, Veksler O, Boykov Y (2013) Grabcut in one cut. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1769\u20131776","DOI":"10.1109\/ICCV.2013.222"},{"issue":"7","key":"1207_CR80","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.3390\/app13074516","volume":"13","author":"Y Tang","year":"2023","unstructured":"Tang Y, Deng J, Guo Z (2023) Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation. Appl Sci 13(7):4516","journal-title":"Appl Sci"},{"key":"1207_CR81","unstructured":"Tansley S, Tolle KM (2009) In: Hey AJ (ed) The Fourth Paradigm: Data-intensive Scientific Discovery, vol. 1. Microsoft Research, Redmond"},{"key":"1207_CR82","doi-asserted-by":"crossref","unstructured":"Tian M, Ma K, Liu Z, Qiu Q, Tan Y, Xie Z (2022) Recognition of geological legends on a geological profile via an improved deep learning method with augmented data using transfer learning strategies. Ore Geol Rev: 105270","DOI":"10.1016\/j.oregeorev.2022.105270"},{"key":"1207_CR83","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.cageo.2017.12.007","volume":"112","author":"C Wang","year":"2018","unstructured":"Wang C, Ma X, Chen J, Chen J (2018) Information extraction and knowledge graph construction from geoscience literature. Comput Geosci 112:112\u2013120","journal-title":"Comput Geosci"},{"key":"1207_CR84","volume":"134","author":"B Wang","year":"2021","unstructured":"Wang B, Wu L, Li W, Qiu Q, Xie Z, Liu H, Zhou Y (2021) A semi-automatic approach for generating geological profiles by integrating multi-source data. Ore Geol Rev 134:104190","journal-title":"Ore Geol Rev"},{"key":"1207_CR85","doi-asserted-by":"crossref","unstructured":"Wang R, Liu W, McDonald C (2015) Using word embeddings to enhance keyword identification for scientific publications. In: Databases Theory and Applications. Springer, pp 257\u2013268","DOI":"10.1007\/978-3-319-19548-3_21"},{"key":"1207_CR86","unstructured":"Wang DH, Wang RJ, Sun Y, Li JK, Zhao Z, Zhao T, ... Zhou YY (2016) A review of achievements in the three-type rare mineral resources (rare resources, rare earth and rarely scattered resources) survey in China. Acta Geosci Sin (5):569\u2013580"},{"key":"1207_CR87","doi-asserted-by":"crossref","unstructured":"Wang B, Ma K, Wu L, Qiu Q, Xie Z, Tao L (2022a) Visual analytics and information extraction of geological content for text-based mineral exploration reports. Ore Geol Rev: 104818","DOI":"10.1016\/j.oregeorev.2022.104818"},{"key":"1207_CR88","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2022.105229","volume":"168","author":"B Wang","year":"2022","unstructured":"Wang B, Wu L, Xie Z, Qiu Q, Zhou Y, Ma K, Tao L (2022b) Understanding geological reports based on knowledge graphs using a deep learning approach. Comput Geosci 168:105229","journal-title":"Comput Geosci"},{"key":"1207_CR89","doi-asserted-by":"crossref","unstructured":"Wang C, Li Y, Chen J, Ma X (2022) named entity annotation schema for geological literature mining in the domain of porphyry copper deposits. Ore Geol Rev: 105243","DOI":"10.1016\/j.oregeorev.2022.105243"},{"key":"1207_CR90","doi-asserted-by":"crossref","unstructured":"Wang T, Zheng L, Lv H, Zhou C, Shen Y, Qiu Q, ... Wang G (2023) A distributed joint extraction framework for sedimentological entities and relations with federated learning. Expert Syst Appl 213:119216","DOI":"10.1016\/j.eswa.2022.119216"},{"key":"1207_CR91","doi-asserted-by":"crossref","unstructured":"Wei D, Liu Z, Xu D, Ma K, Tao L, Xie Z, ... Pan S (2022) GeoBERTSegmenter: Word segmentation of Chinese texts in the geoscience domain using the improved BERT model. Earth Space Sci 9(10):e2022EA002511","DOI":"10.1029\/2022EA002511"},{"issue":"4","key":"1207_CR92","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/2333112.2333115","volume":"44","author":"W Wong","year":"2012","unstructured":"Wong W, Liu W, Bennamoun M (2012) Ontology learning from text: a look back and into the future. ACM Comput Surveys (CSUR) 44(4):20","journal-title":"ACM Comput Surveys (CSUR)"},{"issue":"4","key":"1207_CR93","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s00138-019-01017-9","volume":"30","author":"S Woo","year":"2019","unstructured":"Woo S, Hwang S, Jang HD, Kweon IS (2019) Gated bidirectional feature pyramid network for accurate one-shot detection. Mach vis Appl 30(4):543\u2013555","journal-title":"Mach vis Appl"},{"issue":"9","key":"1207_CR94","doi-asserted-by":"crossref","first-page":"13261","DOI":"10.1007\/s10668-020-01209-8","volume":"23","author":"W Wu","year":"2021","unstructured":"Wu W, Zhou J, Niu J, Lv H (2021) Study on coupling between mineral resources exploitation and the mining ecological environment in Shanxi Province. Environ Dev Sustain 23(9):13261\u201313283","journal-title":"Environ Dev Sustain"},{"key":"1207_CR95","doi-asserted-by":"crossref","unstructured":"Wu L, Xue L, Li C, Lv X, Chen Z, Jiang B, ... Xie Z (2017) A knowledge-driven geospatially enabled framework for geological big data. ISPRS Int J Geo-Inf 6(6):166","DOI":"10.3390\/ijgi6060166"},{"issue":"1","key":"1207_CR96","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.jcp.2006.03.026","volume":"219","author":"Y Xiang","year":"2006","unstructured":"Xiang Y, Chung AC, Ye J (2006) An active contour model for image segmentation based on elastic interaction. J Comput Phys 219(1):455\u2013476","journal-title":"J Comput Phys"},{"key":"1207_CR97","doi-asserted-by":"crossref","unstructured":"Yang D, Wang S, Li Z (2018) Ensemble neural relation extraction with adaptive boosting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. IJCAI\u201918 AAAI Press, pp 4532\u20134538. http:\/\/dl.acm.org\/citation.cfm?id=3304222.3304400","DOI":"10.24963\/ijcai.2018\/630"},{"key":"1207_CR98","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.engappai.2017.06.024","volume":"64","author":"L Yao","year":"2017","unstructured":"Yao L, Zhang Y, Chen Q, Qian H, Hu Z (2017) Mining coherent topics in documents using word embeddings and large-scale text data. Eng Appl Artif Intell 64:432\u2013439","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"1207_CR99","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/83.366472","volume":"4","author":"JC Yen","year":"1995","unstructured":"Yen JC, Chang FJ, Chang S (1995) A new criterion for automatic multilevel thresholding. IEEE Trans Image Process 4(3):370\u2013378","journal-title":"IEEE Trans Image Process"},{"key":"1207_CR100","volume":"231","author":"B Yin","year":"2021","unstructured":"Yin B, Zuo R, Xiong Y, Li Y, Yang W (2021) Knowledge discovery of geochemical patterns from a data-driven perspective. J Geochem Explor 231:106872","journal-title":"J Geochem Explor"},{"key":"1207_CR101","doi-asserted-by":"crossref","unstructured":"Yu Y, Wang Y, Mu J, Li W, Jiao S, Wang Z, ... Zhu Y (2022) Chinese mineral named entity recognition based on BERT model. Expert Syst Appl 206:117727","DOI":"10.1016\/j.eswa.2022.117727"},{"issue":"2","key":"1207_CR102","first-page":"343","volume":"34","author":"XY Zhang","year":"2018","unstructured":"Zhang XY, Ye P, Wang S, Du M (2018a) Geological entity recognition method based on Deep Belief Networks. Acta Petrol Sin 34(2):343\u2013351","journal-title":"Acta Petrol Sin"},{"key":"1207_CR103","doi-asserted-by":"crossref","unstructured":"Zhang C, Zhang X, Jiang W, Shen Q, Zhang S (2009) Rule-based extraction of spatial relations in natural language text. In: 2009 International Conference on Computational Intelligence and Software Engineering. IEEE, pp 1\u20134","DOI":"10.1109\/CISE.2009.5363900"},{"key":"1207_CR104","doi-asserted-by":"crossref","unstructured":"Zhang Y, Chen M, Liu L (2015) A review on text mining. In: Proceedings of the 6th IEEE International Conference on Software Engineering and Service Science (ICSESS) IEEE, pp 681\u2013685","DOI":"10.1109\/ICSESS.2015.7339149"},{"key":"1207_CR105","doi-asserted-by":"crossref","unstructured":"Zhang Q, Liu X, Fu J (2018b) Neural networks incorporating dictionaries for Chinese word segmentation","DOI":"10.1609\/aaai.v32i1.11959"},{"key":"1207_CR106","doi-asserted-by":"crossref","unstructured":"Zhang J, Qian L, Wang S, Zhu Y, Gao Z, Yu H, Li W (2023) A Levenshtein distance-based method for word segmentation in corpus augmentation of geoscience texts. Ann GIS: 1\u201314","DOI":"10.1080\/19475683.2023.2165543"},{"key":"1207_CR107","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"issue":"2\u20133","key":"1207_CR108","first-page":"400","volume":"39","author":"T Zhao","year":"2020","unstructured":"Zhao T, Wang A, Liu C (2020) Design and implementation of mineral resources security dynamic analysis system based on Big Data of National Mineral Resources Survey. Geol Bull China 39(2\u20133):400\u2013405","journal-title":"Geol Bull China"},{"key":"1207_CR109","doi-asserted-by":"crossref","unstructured":"Zhou C, Wang H, Wang C, Hou Z, Zheng Z, Shen S, ... Zhu Y (2021) Geoscience knowledge graph in the big data era. Sci China Earth Sci 64(7):1105\u20131114","DOI":"10.1007\/s11430-020-9750-4"},{"key":"1207_CR110","doi-asserted-by":"crossref","unstructured":"Zhu Y, Zhou W, Xu Y, Liu J, Tan Y (2017) Intelligent learning for knowledge graph towards geological data.\u00a0Sci Program 2017","DOI":"10.1155\/2017\/5072427"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01207-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-023-01207-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01207-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T09:19:17Z","timestamp":1717406357000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-023-01207-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":110,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1207"],"URL":"https:\/\/doi.org\/10.1007\/s12145-023-01207-0","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]},"assertion":[{"value":"15 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}