{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T11:49:02Z","timestamp":1773229742880,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Major Scientific and Technological Special Project of Guizhou Province, China","award":["No.[2024]014"],"award-info":[{"award-number":["No.[2024]014"]}]},{"name":"Guizhou Provincial Basic Research Program","award":["No.MS[2025]686"],"award-info":[{"award-number":["No.MS[2025]686"]}]},{"name":"Guizhou Provincial Key Technology R&D Program","award":["No.PA[2025]004"],"award-info":[{"award-number":["No.PA[2025]004"]}]},{"name":"Research Project for Recruited Talents at Guizhou University","award":["No.GDRJH[2024]15"],"award-info":[{"award-number":["No.GDRJH[2024]15"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s44443-025-00416-5","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:33:33Z","timestamp":1766122413000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-grained named entity recognition of ancient texts based on multi-view semantic fusion and deep learning"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9648-9506","authenticated-orcid":false,"given":"Xiuzhang","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yuling","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Chaofan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Di","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Xuanzhang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Ruijie","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Binyang","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"issue":"7900","key":"416_CR1","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1038\/s41586-022-04448-z","volume":"603","author":"Y Assael","year":"2022","unstructured":"Assael Y, Sommerschield T, Shillingford B et al (2022) Restoring and attributing ancient texts using deep neural networks. Nature 603(7900):280\u2013283","journal-title":"Nature"},{"key":"416_CR2","doi-asserted-by":"crossref","unstructured":"Assael Y, Sommerschield T, Prag J (2019) Restoring ancient text using deep learning: a case study on Greek epigraphy. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 6368\u20136375","DOI":"10.18653\/v1\/D19-1668"},{"key":"416_CR3","doi-asserted-by":"crossref","unstructured":"Duan S, Wang J, Su Q (2024) Restoring ancient ideograph: a multimodal multitask neural network approach. In: Proceedings of LREC-COLING, pp 14005\u201314015","DOI":"10.63317\/5a72ktj6w4t5"},{"issue":"2","key":"416_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3604931","volume":"56","author":"M Ehrmann","year":"2023","unstructured":"Ehrmann M, Hamdi A, Pontes EL et al (2023) Named entity recognition and classification in historical documents: a survey. ACM Comput Surv 56(2):1\u201347","journal-title":"ACM Comput Surv"},{"issue":"1","key":"416_CR5","doi-asserted-by":"publisher","first-page":"102753","DOI":"10.1016\/j.ipm.2021.102753","volume":"59","author":"T Fan","year":"2022","unstructured":"Fan T, Wang H (2022) Research of Chinese intangible cultural heritage knowledge graph construction and attribute value extraction with graph attention network. Inform Process Manag 59(1):102753","journal-title":"Inform Process Manag"},{"key":"416_CR6","doi-asserted-by":"crossref","unstructured":"Fang J, Wang X, Meng Z, et al (2023) MANNER: a variational memory-augmented model for cross domain few-shot named entity recognition. In: Proceedings of the 61st annual meeting of the association for computational linguistics, pp 4261\u20134276","DOI":"10.18653\/v1\/2023.acl-long.234"},{"key":"416_CR7","doi-asserted-by":"publisher","first-page":"107409","DOI":"10.1016\/j.knosys.2021.107409","volume":"232","author":"T Gao","year":"2021","unstructured":"Gao T, Zhu S, Liu J et al (2021) A new context-aware approach for automatic Chinese poetry generation. Knowl-Based Syst 232:107409","journal-title":"Knowl-Based Syst"},{"key":"416_CR8","doi-asserted-by":"crossref","unstructured":"Ge S (2022) Integration of named entity recognition and sentence segmentation on ancient Chinese based on Siku-BERT. In Proceedings of the 2nd international workshop on natural language processing for digital humanities, pp 167\u2013173","DOI":"10.18653\/v1\/2022.nlp4dh-1.21"},{"key":"416_CR9","doi-asserted-by":"crossref","unstructured":"Guo G, Yang J, Lu F, et al (2023) Towards effective ancient chinese translation: dataset, model, and evaluation. In: Proceedings of international conference on natural language processing and chinese computing, pp 416-427","DOI":"10.1007\/978-3-031-44696-2_33"},{"key":"416_CR10","doi-asserted-by":"publisher","first-page":"103422","DOI":"10.1016\/j.jbi.2020.103422","volume":"107","author":"X Li","year":"2020","unstructured":"Li X, Zhang H, Zhou XH (2020) Chinese clinical named entity recognition with variant neural structures based on BERT methods. J Biomed Inform 107:103422","journal-title":"J Biomed Inform"},{"issue":"4","key":"416_CR11","doi-asserted-by":"publisher","first-page":"186","DOI":"10.3390\/info11040186","volume":"11","author":"S Liu","year":"2020","unstructured":"Liu S, Yang H, Li J et al (2020) Preliminary study on the knowledge graph construction of Chinese ancient history and culture. Information 11(4):186","journal-title":"Information"},{"key":"416_CR12","doi-asserted-by":"publisher","first-page":"106958","DOI":"10.1016\/j.knosys.2021.106958","volume":"221","author":"J Liu","year":"2021","unstructured":"Liu J, Gao L, Guo S et al (2021) A hybrid deep-learning approach for complex biochemical named entity recognition. Knowl-Based Syst 221:106958","journal-title":"Knowl-Based Syst"},{"issue":"15","key":"416_CR13","first-page":"13452","volume":"35","author":"Z Liu","year":"2021","unstructured":"Liu Z, Xu Y, Yu T et al (2021) Crossner: evaluating cross-domain named entity recognition. Proc AAAI Conf Artif Intell 35(15):13452\u201313460","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"416_CR14","doi-asserted-by":"publisher","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 et al (2022) Chinese named entity recognition: the state of the art. Neurocomputing 473:37\u201353","journal-title":"Neurocomputing"},{"issue":"3","key":"416_CR15","doi-asserted-by":"publisher","first-page":"103290","DOI":"10.1016\/j.ipm.2023.103290","volume":"60","author":"Y Liu","year":"2023","unstructured":"Liu Y, Huang S, Li R et al (2023) USAF: multimodal Chinese named entity recognition using synthesized acoustic features. Inform Process Manag 60(3):103290","journal-title":"Inform Process Manag"},{"key":"416_CR16","doi-asserted-by":"publisher","first-page":"111735","DOI":"10.1016\/j.knosys.2024.111735","volume":"294","author":"P Lv","year":"2024","unstructured":"Lv P, Zhang Q, Wu M et al (2024) Intelligent extraction of medical entity relationship based on graph neural network and optimization strategy. Knowl-Based Syst 294:111735","journal-title":"Knowl-Based Syst"},{"issue":"21","key":"416_CR17","first-page":"23808","volume":"38","author":"HY Ma","year":"2024","unstructured":"Ma HY, Huang HH, Liu CL (2024) Reading between the lines: image-based order detection in OCR for Chinese historical documents. Proc AAAI Conf Artif Intell 38(21):23808\u201323810","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"1","key":"416_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3445965","volume":"54","author":"Z Nasar","year":"2021","unstructured":"Nasar Z, Jaffry SW, Malik MK (2021) Named entity recognition and relation extraction: State-of-the-art. ACM Comput Surv 54(1):1\u201339","journal-title":"ACM Comput Surv"},{"key":"416_CR19","doi-asserted-by":"crossref","unstructured":"Qi Y, Ma H, Shi L, et al (2022) Adversarial transfer for classical Chinese NER with translation word segmentation. In: Proceedings of international conference on natural language processing and chinese computing, pp 298\u2013310","DOI":"10.1007\/978-3-031-17120-8_24"},{"key":"416_CR20","doi-asserted-by":"publisher","first-page":"121925","DOI":"10.1016\/j.eswa.2023.121925","volume":"238","author":"Q Qiu","year":"2024","unstructured":"Qiu Q, Tian M, Huang Z et al (2024) Chinese engineering geological named entity recognition by fusing multi-features and data enhancement using deep learning. Expert Syst Appl 238:121925","journal-title":"Expert Syst Appl"},{"key":"416_CR21","doi-asserted-by":"crossref","unstructured":"Shen Y, Ma X, Tan Z, et al (2021) Locate and label: a two-stage identifier for nested named entity recognition. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pp 2782\u20132794","DOI":"10.18653\/v1\/2021.acl-long.216"},{"issue":"3","key":"416_CR22","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1162\/coli_a_00481","volume":"49","author":"T Sommerschield","year":"2023","unstructured":"Sommerschield T, Assael Y, Pavlopoulos J et al (2023) Machine learning for ancient languages: a survey. Comput Linguist 49(3):703\u2013747","journal-title":"Comput Linguist"},{"key":"416_CR23","doi-asserted-by":"crossref","unstructured":"Swindall MI, Player T, Keener B et al (2022) Dataset augmentation in papyrology with generative models: a study of synthetic ancient greek character images. In: Proceedings of the 2022 International Joint Conference on Artificial Intelligence (IJCAI), pp 4973\u20134979","DOI":"10.24963\/ijcai.2022\/689"},{"issue":"1","key":"416_CR24","doi-asserted-by":"publisher","first-page":"64","DOI":"10.23919\/cje.2022.00.077","volume":"32","author":"M Tang","year":"2023","unstructured":"Tang M, Xie S, Liu X (2023) Ancient character recognition: a novel image dataset of Shui manuscript characters and classification model. Chin J Electron 32(1):64\u201375","journal-title":"Chin J Electron"},{"key":"416_CR25","doi-asserted-by":"publisher","first-page":"110114","DOI":"10.1016\/j.knosys.2022.110114","volume":"260","author":"X Wang","year":"2023","unstructured":"Wang X, Liu J (2023) A novel feature integration and entity boundary detection for named entity recognition in cybersecurity. Knowl-Based Syst 260:110114","journal-title":"Knowl-Based Syst"},{"key":"416_CR26","doi-asserted-by":"publisher","first-page":"107475","DOI":"10.1016\/j.asoc.2021.107475","volume":"108","author":"K Wang","year":"2021","unstructured":"Wang K, Yi Y, Tang Z et al (2021) Multi-scene ancient Chinese text recognition with deep coupled alignments. Appl Soft Comput 108:107475","journal-title":"Appl Soft Comput"},{"key":"416_CR27","doi-asserted-by":"crossref","unstructured":"Wang X, Jiang Y, Bach N, et al (2021) Improving named entity recognition by external context retrieving and cooperative learning. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pp 1800\u20131812","DOI":"10.18653\/v1\/2021.acl-long.142"},{"key":"416_CR28","doi-asserted-by":"crossref","unstructured":"Wang J, Shou L, Chen K, et al (2020) Pyramid: a layered model for nested named entity recognition. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 5918\u20135928","DOI":"10.18653\/v1\/2020.acl-main.525"},{"issue":"2","key":"416_CR29","first-page":"97","volume":"4","author":"Y Wu","year":"2022","unstructured":"Wu Y, Zhang W, Zhang W et al (2022) Named Entity Recognition of Ancient Wine Texts Based on Deep Learning Models. Acad J Sci Technol 4(2):97\u2013103","journal-title":"Acad J Sci Technol"},{"issue":"1","key":"416_CR30","doi-asserted-by":"publisher","first-page":"17488","DOI":"10.1038\/s41598-024-68561-x","volume":"14","author":"Y Xu","year":"2024","unstructured":"Xu Y, Mao C, Wang Z et al (2024) Semantic-enhanced graph neural network for named entity recognition in ancient Chinese books. Sci Rep 14(1):17488","journal-title":"Sci Rep"},{"key":"416_CR31","unstructured":"Yadav V, Bethard S (2019) A survey on recent advances in named entity recognition from deep learning models, arXiv:1910.11470"},{"issue":"11","key":"416_CR32","doi-asserted-by":"publisher","first-page":"202","DOI":"10.23919\/JCC.fa.2022-0509.202311","volume":"20","author":"X Yang","year":"2023","unstructured":"Yang X, Peng G, Zhang D et al (2023) PowerDetector: malicious PowerShell script family classification based on multi-modal semantic fusion and deep learning. China Commun 20(11):202\u2013224","journal-title":"China Commun"},{"key":"416_CR33","doi-asserted-by":"publisher","first-page":"111762","DOI":"10.1016\/j.knosys.2024.111762","volume":"296","author":"K Yang","year":"2024","unstructured":"Yang K, Yang Z, Zhao S et al (2024) Uncertainty-Aware Contrastive Learning for semi-supervised named entity recognition. Knowl-Based Syst 296:111762","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"416_CR34","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/TVCG.2020.3028975","volume":"27","author":"T Yousef","year":"2020","unstructured":"Yousef T, Janicke S (2020) A survey of text alignment visualization. IEEE Trans Visual Comput Graphics 27(2):1149\u20131159","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"416_CR35","doi-asserted-by":"publisher","first-page":"111266","DOI":"10.1016\/j.knosys.2023.111266","volume":"284","author":"Y Yu","year":"2024","unstructured":"Yu Y, Wang Z, Wei W et al (2024) Exploiting global contextual information for document-level named entity recognition. Knowl-Based Syst 284:111266","journal-title":"Knowl-Based Syst"},{"key":"416_CR36","doi-asserted-by":"crossref","unstructured":"Yu J, Bohnet B, Poesio M (2020) Named entity recognition as dependency parsing. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 6470\u20136476","DOI":"10.18653\/v1\/2020.acl-main.577"},{"key":"416_CR37","doi-asserted-by":"crossref","unstructured":"Yu J, Jiang J, Yang L, et al (2020) Improving multimodal named entity recognition via entity span detection with unified multimodal transformer. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3342\u20133352","DOI":"10.18653\/v1\/2020.acl-main.306"},{"key":"416_CR38","doi-asserted-by":"crossref","unstructured":"Yu P, Wang X (2020) BERT-based named entity recognition in Chinese twenty-four histories. In: Proceedings of international conference on web information systems and applications, pp 289-301","DOI":"10.1007\/978-3-030-60029-7_27"},{"key":"416_CR39","doi-asserted-by":"publisher","first-page":"111730","DOI":"10.1016\/j.knosys.2024.111730","volume":"295","author":"E Zha","year":"2024","unstructured":"Zha E, Zeng D, Lin M et al (2024) Ceptner: contrastive learning enhanced prototypical network for two-stage few-shot named entity recognition. Knowl-Based Syst 295:111730","journal-title":"Knowl-Based Syst"},{"issue":"16","key":"416_CR40","first-page":"14347","volume":"35","author":"D Zhang","year":"2021","unstructured":"Zhang D, Wei S, Li S et al (2021) Multi-modal graph fusion for named entity recognition with targeted visual guidance. Proc AAAI Conf Artif intell 35(16):14347\u201314355","journal-title":"Proc AAAI Conf Artif intell"},{"key":"416_CR41","doi-asserted-by":"publisher","first-page":"109178","DOI":"10.1016\/j.knosys.2022.109178","volume":"251","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Zhang H, Wan Q et al (2022) Lelner: a lightweight and effective low-resource named entity recognition model. Knowl-Based Syst 251:109178","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"416_CR42","doi-asserted-by":"publisher","first-page":"103314","DOI":"10.1016\/j.ipm.2023.103314","volume":"60","author":"B Zhang","year":"2023","unstructured":"Zhang B, Cai J, Zhang H et al (2023) VisPhone: Chinese named entity recognition model enhanced by visual and phonetic features. Inform Process Manag 60(3):103314","journal-title":"Inform Process Manag"},{"key":"416_CR43","doi-asserted-by":"crossref","unstructured":"Zhang H, Zhu H, Ruan J, et al (2021) People name recognition from ancient Chinese literature using distant supervision and deep learning. In: Proceedings of the 2nd international conference on artificial intelligence and information systems, pp 1\u20136","DOI":"10.1145\/3469213.3470270"},{"issue":"11","key":"416_CR44","first-page":"14011","volume":"37","author":"S Zhao","year":"2023","unstructured":"Zhao S, Wang CY, Hu M et al (2023) MCL: multi-granularity contrastive learning framework for Chinese NER. Proc AAAI Conf Artif Intell 37(11):14011\u201314019","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"416_CR45","doi-asserted-by":"crossref","unstructured":"Zhao L, Feng Z, Sun N, et al (2023) ENER: named entity recognition model for ethnic ancient books based on entity boundary detection. In Proceedings of international conference on cognitive computing, pp 47\u201359","DOI":"10.1007\/978-3-031-51671-9_4"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00416-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00416-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00416-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:38:59Z","timestamp":1773153539000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00416-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,19]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["416"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00416-5","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,19]]},"assertion":[{"value":"20 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2025","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"28"}}