{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T04:10:29Z","timestamp":1749010229402,"version":"3.41.0"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["KYCX24_0684"],"award-info":[{"award-number":["KYCX24_0684"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07442-0","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T11:02:39Z","timestamp":1748948559000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A discontinuous NER model based on token prediction and contrastive learning to enhance span"],"prefix":"10.1007","volume":"81","author":[{"given":"Yaodi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dianying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenxi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohe","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"7442_CR1","doi-asserted-by":"publisher","first-page":"100017","DOI":"10.1016\/j.nlp.2023.100017","volume":"3","author":"B Jehangir","year":"2023","unstructured":"Jehangir B, Radhakrishnan S, Agarwal R (2023) A survey on named entity recognition\u2014datasets, tools, and methodologies. Nat Lang Process J 3:100017. https:\/\/doi.org\/10.1016\/j.nlp.2023.100017","journal-title":"Nat Lang Process J"},{"key":"7442_CR2","doi-asserted-by":"publisher","unstructured":"Sharma A, Amrita, Chakraborty S, Kumar S (2022) Named entity recognition in natural language processing: a systematic review. In: Proceedings of Second Doctoral Symposium on Computational Intelligence. Advances in Intelligent Systems and Computing, Springer, Singapore, vol 1374, pp 817\u2013828. https:\/\/doi.org\/10.1007\/978-981-16-3346-1_66","DOI":"10.1007\/978-981-16-3346-1_66"},{"key":"7442_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cag.2021.12.003","volume":"103","author":"T Munz","year":"2022","unstructured":"Munz T, V\u00e4th D, Kuznecov P, Vu NT, Weiskopf D (2022) Visualization-based improvement of neural machine translation. Comput Graph 103:45\u201360. https:\/\/doi.org\/10.1016\/j.cag.2021.12.003","journal-title":"Comput Graph"},{"key":"7442_CR4","doi-asserted-by":"publisher","first-page":"1512329","DOI":"10.3389\/fmed.2024.1512329","volume":"11","author":"Y Duan","year":"2025","unstructured":"Duan Y, Zhou Q, Li Y, Qin C, Wang Z, Kan H, Hu J (2025) Research on a traditional Chinese medicine case-based question-answering system integrating large language models and knowledge graphs. Front Med 11:1512329. https:\/\/doi.org\/10.3389\/fmed.2024.1512329","journal-title":"Front Med"},{"key":"7442_CR5","doi-asserted-by":"publisher","unstructured":"Chatterjee S, Dietz L (2021) Entity retrieval using fine-grained entity aspects. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 11\u201315 July, 2021, Virtual Event Canada, SIGIR, pp 1662\u20131666. https:\/\/doi.org\/10.1145\/3404835.3463035","DOI":"10.1145\/3404835.3463035"},{"key":"7442_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2025.104783","author":"A Alhassan","year":"2025","unstructured":"Alhassan A, Schlegel V, Aloud M, Batista-Navarro R, Nenadic G (2025) Discontinuous named entities in clinical text: a systematic literature review. J Biomed Inf. https:\/\/doi.org\/10.1016\/j.jbi.2025.104783","journal-title":"J Biomed Inf"},{"key":"7442_CR7","doi-asserted-by":"publisher","unstructured":"Dai X, Karimi S, Hachey B, Paris C (2020) An effective transition-based model for discontinuous NER. arxiv preprint arxiv:2004.13454. https:\/\/doi.org\/10.48550\/arXiv.2004.13454","DOI":"10.48550\/arXiv.2004.13454"},{"key":"7442_CR8","doi-asserted-by":"publisher","first-page":"121103","DOI":"10.1016\/j.eswa.2023.121103","volume":"234","author":"Y Liu","year":"2023","unstructured":"Liu Y, Wei S, Huang H, Lai Q, Li M, Guan L (2023) Naming entity recognition of citrus pests and diseases based on the BERT-BiLSTM-CRF model. Expert Syst Appl 234:121103. https:\/\/doi.org\/10.1016\/j.eswa.2023.121103","journal-title":"Expert Syst Appl"},{"key":"7442_CR9","unstructured":"Tang B, Chen Q, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H (2015) Recognizing disjoint clinical concepts in clinical text using machine learning-based methods. In: AMIA annual symposium proceedings, American Medical Informatics Association 2015, pp 1184\u20131193"},{"key":"7442_CR10","doi-asserted-by":"publisher","unstructured":"Yan Y, Cai B, Song S (2023) Nested named entity recognition as building local hypergraphs. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol 37(11), pp 13878\u201313886. https:\/\/doi.org\/10.1609\/aaai.v37i11.26625","DOI":"10.1609\/aaai.v37i11.26625"},{"key":"7442_CR11","doi-asserted-by":"publisher","unstructured":"Li J, Fei H, Liu J, Wu S, Zhang M, Teng C, Ji D, Li F (2022) Unified named entity recognition as word-word relation classification. In: Proc AAAI Conf Artif Intel, vol 36(10), pp 10965\u201310973. https:\/\/doi.org\/10.1609\/aaai.v36i10.21344","DOI":"10.1609\/aaai.v36i10.21344"},{"issue":"13","key":"7442_CR12","doi-asserted-by":"publisher","first-page":"7187","DOI":"10.1007\/s00521-024-09454-y","volume":"36","author":"T Mao","year":"2024","unstructured":"Mao T, Xu Y, Liu W, Peng J, Chen L, Zhou M (2024) A simple but effective span-level tagging method for discontinuous named entity recognition. Neural Comput Appl 36(13):7187\u20137201. https:\/\/doi.org\/10.1007\/s00521-024-09454-y","journal-title":"Neural Comput Appl"},{"key":"7442_CR13","doi-asserted-by":"publisher","unstructured":"Yan H, Gui T, Dai J, Guo Q, Zhang Z, Qiu X (2021) A unified generative framework for various NER subtasks. arXiv preprint arXiv:2106.01223. https:\/\/doi.org\/10.48550\/arXiv.2106.01223","DOI":"10.48550\/arXiv.2106.01223"},{"key":"7442_CR14","doi-asserted-by":"publisher","unstructured":"Tian Y, Chen G, Song Y, Wan X (2021) Dependency-driven relation extraction with attentive graph convolutional networks. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Aug, 2021, Online, ACL 2021, pp 4458\u20134471. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.344","DOI":"10.18653\/v1\/2021.acl-long.344"},{"key":"7442_CR15","doi-asserted-by":"publisher","unstructured":"Li F, Lin Z, Zhang M, Ji D (2021) A span-based model for joint overlapped and discontinuous named entity recognition. arXiv preprint arXiv:2106.14373. https:\/\/doi.org\/10.48550\/arXiv.2106.14373","DOI":"10.48550\/arXiv.2106.14373"},{"key":"7442_CR16","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1162\/tacl_a_00602","volume":"11","author":"P Huang","year":"2023","unstructured":"Huang P, Zhao X, Hu M, Tan Z, Xiao W (2023) T2-NER: a two-stage span-based framework for unified named entity recognition with templates. Trans Assoc Comput Linguist 11:1265\u20131282. https:\/\/doi.org\/10.1162\/tacl_a_00602","journal-title":"Trans Assoc Comput Linguist"},{"issue":"12","key":"7442_CR17","doi-asserted-by":"publisher","first-page":"13670","DOI":"10.1007\/s11227-023-05224-0","volume":"79","author":"Y Zhen","year":"2023","unstructured":"Zhen Y, Li Y, Zhang P, Yang Z, Zhao R (2023) Frequent words and syntactic context integrated biomedical discontinuous named entity recognition method. J Supercomput 79(12):13670\u201313695. https:\/\/doi.org\/10.1007\/s11227-023-05224-0","journal-title":"J Supercomput"},{"key":"7442_CR18","doi-asserted-by":"publisher","unstructured":"Yohannes H M, Amagasa T (2022) Named-entity recognition for a low-resource language using pre-trained language model. In: Proceedings of the 37th ACM\/SIGAPP Symposium on Applied Computing, pp 837\u2013844. https:\/\/doi.org\/10.1145\/3477314.3507066","DOI":"10.1145\/3477314.3507066"},{"key":"7442_CR19","unstructured":"Tang B, Wu Y, Jiang M, Denny J C, Xu H (2013) Recognizing and encoding discorder concepts in clinical text using machine learning and vector space model. CLEF (Working Notes), p 665"},{"key":"7442_CR20","unstructured":"Dirkson A, Verberne S, Kraaij W (2021) FuzzyBIO: A proposal for fuzzy representation of discontinuous entities. In: Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, April, 2021, Online, ACL 2021, pp 77\u201382"},{"key":"7442_CR21","doi-asserted-by":"publisher","unstructured":"Khandelwal A, Kar A, Chikka V R, Karlapalem K (2022) Biomedical NER using novel schema and distant supervision. In: Proceedings of the 21st Workshop on Biomedical Language Processing, May, 2022, Dublin, Ireland, ACL 2022, pp 155\u2013160. https:\/\/doi.org\/10.18653\/v1\/2022.bionlp-1.15","DOI":"10.18653\/v1\/2022.bionlp-1.15"},{"key":"7442_CR22","doi-asserted-by":"publisher","unstructured":"Corro C (2024) A fast and sound tagging method for discontinuous named-entity recognition. arXiv preprint arXiv:2409.16243. https:\/\/doi.org\/10.48550\/arXiv.2409.16243","DOI":"10.48550\/arXiv.2409.16243"},{"key":"7442_CR23","doi-asserted-by":"publisher","unstructured":"Muis AO, Lu W (2018) Learning to recognize discontiguous entities. arxiv preprint arxiv:1810.08579. https:\/\/doi.org\/10.18653\/v1\/D16-1008","DOI":"10.18653\/v1\/D16-1008"},{"key":"7442_CR24","doi-asserted-by":"publisher","unstructured":"Wang B, Lu W (2019) Combining spans into entities: a neural two-stage approach for recognizing discontiguous entities. arxiv preprint arxiv:1909.00930. https:\/\/doi.org\/10.48550\/arXiv.1909.00930","DOI":"10.48550\/arXiv.1909.00930"},{"key":"7442_CR25","doi-asserted-by":"publisher","unstructured":"Wang B, Lu W (2018) Neural segmental hypergraphs for overlapping mention recognition. arXiv preprint arXiv:1810.01817. https:\/\/doi.org\/10.48550\/arXiv.1810.01817","DOI":"10.48550\/arXiv.1810.01817"},{"key":"7442_CR26","doi-asserted-by":"publisher","unstructured":"Wang Y, Yu B, Zhu H, Liu T, Yu N, Sun L (2021) Discontinuous named entity recognition as maximal clique discovery. arxiv preprint arxiv:2106.00218. https:\/\/doi.org\/10.48550\/arXiv.2106.00218","DOI":"10.48550\/arXiv.2106.00218"},{"key":"7442_CR27","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1109\/TASLP.2022.3221009","volume":"31","author":"J Liu","year":"2022","unstructured":"Liu J, Ji D, Li J, Teng C, Zhao L, Li F (2022) TOE: a grid-tagging discontinuous NER model enhanced by embedding tag\/word relations and more fine-grained tags. IEEE\/ACM Trans Audio Speech Lang Process 31:177\u2013187. https:\/\/doi.org\/10.1109\/TASLP.2022.3221009","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"7442_CR28","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/978-981-97-5672-8_18","volume":"14878","author":"Y Lu","year":"2024","unstructured":"Lu Y, Yang J, Zhang X, Feng L, Liu H (2024) TCGA: a grid-tagging NER model enhanced by fusing position and region information. Int Conf Intel Comput 14878:209\u2013220. https:\/\/doi.org\/10.1007\/978-981-97-5672-8_18","journal-title":"Int Conf Intel Comput"},{"issue":"5","key":"7442_CR29","doi-asserted-by":"publisher","first-page":"967","DOI":"10.3390\/electronics14050967","volume":"14","author":"H Yu","year":"2025","unstructured":"Yu H, Cui Y, Cao H, Wang H (2025) A discontinuous entity recognition model based on global feature interaction mechanism. Electronics 14(5):967. https:\/\/doi.org\/10.3390\/electronics14050967","journal-title":"Electronics"},{"key":"7442_CR30","doi-asserted-by":"publisher","unstructured":"Cabral R C, Han S C, Alhassan A, Batista-Navarro R, Nenadic G, Poon J (2024) TriG-NER: triplet-grid framework for discontinuous named entity recognition. arXiv preprint arXiv:2411.01839. https:\/\/doi.org\/10.48550\/arXiv.2411.01839","DOI":"10.48550\/arXiv.2411.01839"},{"key":"7442_CR31","doi-asserted-by":"publisher","unstructured":"Zhang S, Shen Y, Tan Z, Wu Y, Lu W (2022) De-bias for generative extraction in unified NER task. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 808\u2013818. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.59","DOI":"10.18653\/v1\/2022.acl-long.59"},{"issue":"30","key":"7442_CR32","doi-asserted-by":"publisher","first-page":"22223","DOI":"10.1007\/s00521-023-08820-6","volume":"35","author":"L Ji","year":"2023","unstructured":"Ji L, Yan D, Cheng Z, Song Y (2023) Improving unified named entity recognition by incorporating mention relevance. Neural Comput Appl 35(30):22223\u201322234. https:\/\/doi.org\/10.1007\/s00521-023-08820-6","journal-title":"Neural Comput Appl"},{"key":"7442_CR33","doi-asserted-by":"publisher","first-page":"184275","DOI":"10.1109\/ACCESS.2024.3424653","volume":"12","author":"Y Zhao","year":"2024","unstructured":"Zhao Y, Ren J (2024) Leveraging multi-level semantic understanding in a unified NER model. IEEE Access 12:184275\u2013184284. https:\/\/doi.org\/10.1109\/ACCESS.2024.3424653","journal-title":"IEEE Access"},{"key":"7442_CR34","doi-asserted-by":"publisher","unstructured":"Zhang X, Tan M, Zhang J, Zhu W (2023) NAG-NER: a unified non-autoregressive generation framework for various NER tasks. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pp 676\u2013686. https:\/\/doi.org\/10.18653\/v1\/2023.acl-industry.65","DOI":"10.18653\/v1\/2023.acl-industry.65"},{"key":"7442_CR35","doi-asserted-by":"publisher","unstructured":"Su J, Yu H (2023) Unified named entity recognition as multi-label sequence generation. In: 2023 International Joint Conference on Neural Networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN54540.2023.10191921","DOI":"10.1109\/IJCNN54540.2023.10191921"},{"key":"7442_CR36","doi-asserted-by":"publisher","unstructured":"Fei H, Ji D, Li B, Liu Y, Ren Y, Li F (2021) Rethinking boundaries: End-to-end recognition of discontinuous mentions with pointer networks. In: Proc AAAI Conf Artif Intel, vol 35(14), pp 12785\u201312793. https:\/\/doi.org\/10.1609\/aaai.v35i14.17513","DOI":"10.1609\/aaai.v35i14.17513"},{"key":"7442_CR37","doi-asserted-by":"publisher","unstructured":"Chen T C, Lin W Y (2024) On fusing ChatGPT and ensemble learning in discon-tinuous named entity recognition in health corpora. arXiv preprint arXiv:2412.16976. https:\/\/doi.org\/10.48550\/arXiv.2412.16976","DOI":"10.48550\/arXiv.2412.16976"},{"key":"7442_CR38","doi-asserted-by":"publisher","unstructured":"Qiao L, Li P, Jin T, Li X (2023) Finding cycles in graph: a unified approach for various NER tasks. In: 2023 International Joint Conference on Neural Networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN54540.2023.10191254","DOI":"10.1109\/IJCNN54540.2023.10191254"},{"key":"7442_CR39","doi-asserted-by":"publisher","unstructured":"He Y, Tang B (2022) SetGNER: general named entity recognition as entity set generation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp 3074\u20133085. https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.200","DOI":"10.18653\/v1\/2022.emnlp-main.200"},{"key":"7442_CR40","doi-asserted-by":"publisher","unstructured":"Zhang P, Mengyue W (2024) Multi-label supervised contrastive learning. In: Proc AAAI Conf Artif Intel, vol 38(15), pp 16786\u201316793. https:\/\/doi.org\/10.1609\/aaai.v38i15.29619","DOI":"10.1609\/aaai.v38i15.29619"},{"key":"7442_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3370633","volume":"62","author":"R Guan","year":"2024","unstructured":"Guan R, Li Z, Tu W, Wang J, Liu Y, Li X, Tang C, Feng R (2024) Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks. IEEE Trans Geosci Remote Sens 62:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2024.3370633","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7442_CR42","doi-asserted-by":"publisher","unstructured":"Guan R, Li Z, Li X, Tang C (2024) Pixel-superpixel contrastive learning and pseudo-label correction for hyperspectral image clustering. In: ICASSP 2024\u20132024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 6795\u20136799. https:\/\/doi.org\/10.1109\/ICASSP48485.2024.10447080","DOI":"10.1109\/ICASSP48485.2024.10447080"},{"key":"7442_CR43","doi-asserted-by":"publisher","unstructured":"Asl J R, Panzade P, Blanco E, Takabi D, Cai Z (2024) Robustsentembed: Robust sentence embeddings using adversarial self-supervised contrastive learning. arXiv preprint arXiv:2403.11082. https:\/\/doi.org\/10.48550\/arXiv.2403.11082","DOI":"10.48550\/arXiv.2403.11082"},{"key":"7442_CR44","doi-asserted-by":"publisher","unstructured":"Mo Y, Yang J, Liu J, Wang Q, Chen R, Wang J, Li Z (2024) MCL-NER: cross-lingual named entity recognition via multi-view contrastive learning. In: Proc AAAI Conf Artif Intel, vol 38(17), pp 18789\u201318797. https:\/\/doi.org\/10.1609\/aaai.v38i17.29843","DOI":"10.1609\/aaai.v38i17.29843"},{"issue":"6","key":"7442_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3678879","volume":"42","author":"J Xu","year":"2024","unstructured":"Xu J, Yu J, Cai Y, Chua TS (2024) Dual contrastive learning for cross-domain named entity recognition. ACM Trans Inf Syst 42(6):1\u201333. https:\/\/doi.org\/10.1145\/3678879","journal-title":"ACM Trans Inf Syst"},{"key":"7442_CR46","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2025.3528416","author":"C Wang","year":"2025","unstructured":"Wang C, Zhao S, Yan T, Song S, Ma W, Liu K, Wang M (2025) Hierarchical label-enhanced contrastive learning for Chinese NER. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2025.3528416","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7442_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126707","author":"Y Liu","year":"2025","unstructured":"Liu Y, Zhang K, Tong R, Cai C, Chen D, Wu X (2025) A two-stage boundary-enhanced contrastive learning approach for nested named entity recognition. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2025.126707","journal-title":"Expert Syst Appl"},{"key":"7442_CR48","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, Shen Y (2024) Ceptner: contrastive learning enhanced prototypical network for two-stage few-shot named entity recognition. Knowl Based Syst 295:111730. https:\/\/doi.org\/10.1016\/j.knosys.2024.111730","journal-title":"Knowl Based Syst"},{"issue":"4","key":"7442_CR49","doi-asserted-by":"publisher","first-page":"103724","DOI":"10.1016\/j.ipm.2024.103724","volume":"61","author":"L Yang","year":"2024","unstructured":"Yang L, Wang Z, Li Z, Na JC, Yu J (2024) An empirical study of multimodal entity-based sentiment analysis with ChatGPT: improving in-context learning via entity-aware contrastive learning. Inf Process Manag 61(4):103724. https:\/\/doi.org\/10.1016\/j.ipm.2024.103724","journal-title":"Inf Process Manag"},{"key":"7442_CR50","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, Yang Z, Zhang S, Chen H (2024) Uncertainty-aware contrastive learning for semi-supervised named entity recognition. Knowl Based Syst 296:111762. https:\/\/doi.org\/10.1016\/j.knosys.2024.111762","journal-title":"Knowl Based Syst"},{"key":"7442_CR51","doi-asserted-by":"publisher","unstructured":"Zhang H, Zhuang Y (2024) A unified label-aware contrastive learning framework for few-shot named entity recognition. arXiv preprint arXiv:2404.17178. https:\/\/doi.org\/10.48550\/arXiv.2404.17178","DOI":"10.48550\/arXiv.2404.17178"},{"key":"7442_CR52","doi-asserted-by":"publisher","first-page":"129081","DOI":"10.1016\/j.neucom.2024.129081","volume":"618","author":"X Liu","year":"2025","unstructured":"Liu X, Luo S, Wu Z, Pan L, Li X (2025) Joint contrastive learning with semantic enhanced label referents for few-shot NER. Neurocomputing 618:129081. https:\/\/doi.org\/10.1016\/j.neucom.2024.129081","journal-title":"Neurocomputing"},{"key":"7442_CR53","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neunet.2021.02.019","volume":"142","author":"D Jiang","year":"2021","unstructured":"Jiang D, Ren H, Cai Y, Xu J, Liu Y, Leung HF (2021) Candidate region aware nested named entity recognition. Neural Netw 142:340\u2013350. https:\/\/doi.org\/10.1016\/j.neunet.2021.02.019","journal-title":"Neural Netw"},{"key":"7442_CR54","doi-asserted-by":"publisher","unstructured":"Koroteev M V (2021) BERT: a review of applications in natural language processing and understanding. arXiv preprint arXiv:2103.11943. https:\/\/doi.org\/10.48550\/arXiv.2103.11943","DOI":"10.48550\/arXiv.2103.11943"},{"issue":"6","key":"7442_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-025-11162-5","volume":"58","author":"NM Gardazi","year":"2025","unstructured":"Gardazi NM, Daud A, Malik MK, Bukhari A, Alsahfi T, Alshemaimri B (2025) BERT applications in natural language processing: a review. Artif Intell Rev 58(6):1\u201349. https:\/\/doi.org\/10.1007\/s10462-025-11162-5","journal-title":"Artif Intell Rev"},{"key":"7442_CR56","doi-asserted-by":"publisher","first-page":"116682","DOI":"10.1016\/j.eswa.2022.116682","volume":"196","author":"D Li","year":"2022","unstructured":"Li D, Yan L, Yang J, Ma Z (2022) Dependency syntax guided bert-bilstm-gam-crf for chinese ner. Expert Syst Appl 196:116682. https:\/\/doi.org\/10.1016\/j.eswa.2022.116682","journal-title":"Expert Syst Appl"},{"key":"7442_CR57","doi-asserted-by":"publisher","first-page":"126130","DOI":"10.1016\/j.eswa.2024.126130","volume":"266","author":"W Jia","year":"2025","unstructured":"Jia W, Ma R, Yan L, Niu W, Ma Z (2025) Joint entity and relation extraction with table filling based on graph convolutional networks. Expert Syst Appl 266:126130. https:\/\/doi.org\/10.1016\/j.eswa.2024.126130","journal-title":"Expert Syst Appl"},{"key":"7442_CR58","doi-asserted-by":"publisher","first-page":"111040","DOI":"10.1016\/j.knosys.2023.111040","volume":"280","author":"X Peng","year":"2023","unstructured":"Peng X, Sun J, Yan M, Sun F, Wang F (2023) Attention-guided graph convolutional network for multi-behavior recommendation. Knowl Based Syst 280:111040. https:\/\/doi.org\/10.1016\/j.knosys.2023.111040","journal-title":"Knowl Based Syst"},{"key":"7442_CR59","doi-asserted-by":"publisher","first-page":"128645","DOI":"10.1016\/j.neucom.2024.128645","volume":"610","author":"H Hu","year":"2024","unstructured":"Hu H, Wang X, Zhang Y, Chen Q, Guan Q (2024) A comprehensive survey on contrastive learning. Neurocomputing 610:128645. https:\/\/doi.org\/10.1016\/j.neucom.2024.128645","journal-title":"Neurocomputing"},{"key":"7442_CR60","doi-asserted-by":"publisher","unstructured":"Yan Y, Li R, Wang S, Zhang F, Wu W, Xu W (2021) Consert: a contrastive framework for self-supervised sentence representation transfer. arXiv preprint arXiv:2105.11741. https:\/\/doi.org\/10.48550\/arXiv.2105.11741","DOI":"10.48550\/arXiv.2105.11741"},{"key":"7442_CR61","doi-asserted-by":"publisher","unstructured":"Gao T, Yao X, Chen D (2021) Simcse: simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821. https:\/\/doi.org\/10.48550\/arXiv.2104.08821","DOI":"10.48550\/arXiv.2104.08821"},{"key":"7442_CR62","doi-asserted-by":"publisher","unstructured":"Liu Y, Zhang K, Tong R, Cai C, Chen D, Wu X (2024) A flat-span contrastive learning method for nested named entity recognition. In: 2024 International Conference on Asian Language Processing (IALP), pp 37\u201342. https:\/\/doi.org\/10.1109\/IALP63756.2024.10661137","DOI":"10.1109\/IALP63756.2024.10661137"},{"issue":"9","key":"7442_CR63","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1145\/362342.362367","volume":"16","author":"C Bron","year":"1973","unstructured":"Bron C, Kerbosch J (1973) Algorithm 457: finding all cliques of an undirected graph. Commun ACM 16(9):575\u2013577. https:\/\/doi.org\/10.1145\/362342.362367","journal-title":"Commun ACM"},{"issue":"12","key":"7442_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3663363","volume":"56","author":"S Chen","year":"2021","unstructured":"Chen S, Zhang Y, Yang Q (2021) Multi-task learning in natural language processing: an overview. ACM Comput Surv 56(12):1\u201332. https:\/\/doi.org\/10.1145\/3663363","journal-title":"ACM Comput Surv"},{"key":"7442_CR65","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jbi.2015.03.010","volume":"55","author":"S Karimi","year":"2015","unstructured":"Karimi S, Metke-Jimenez A, Kemp M, Wang C (2015) Cadec: a corpus of adverse drug event annotations. J Biomed Inform 55:73\u201381. https:\/\/doi.org\/10.1016\/j.jbi.2015.03.010","journal-title":"J Biomed Inform"},{"key":"7442_CR66","unstructured":"Pradhan S, Elhadad N, South BR, Martinez D, Christensen LM, Vogel A, Suominen H, Chapman, W, & Savova, G. (2013). Task 1: ShARe\/CLEF eHealth evaluation lab 2013. CLEF (working notes), p 1179"},{"key":"7442_CR67","unstructured":"Mowery DL, Velupillai S, South BR, Christensen L, Martinez D, Kelly L, Goeuriot L, Elhadad N, Pradhan S, Chapman W (2014) Task 2: ShARe\/CLEF eHealth evaluation lab 2014. In: Proceedings of CLEF 2014, Sep 2014, Sheffield, United Kingdom, HAL, pp 31\u201342"},{"key":"7442_CR68","doi-asserted-by":"crossref","unstructured":"Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. arxiv preprint arxiv:1603.01360","DOI":"10.18653\/v1\/N16-1030"},{"key":"7442_CR69","unstructured":"Metke-Jimenez A, Karimi S (2016) Concept identification and normalisation for adverse drug event discovery in medical forums. In: BMDID@ ISWC"},{"key":"7442_CR70","doi-asserted-by":"publisher","first-page":"2379208","DOI":"10.1155\/2018\/2379208","volume":"1","author":"B Tang","year":"2018","unstructured":"Tang B, Hu J, Wang X, Chen Q (2018) Recognizing continuous and discontinuous adverse drug reaction mentions from social media using LSTM-CRF. Wirel Commun Mob Comput 1:2379208. https:\/\/doi.org\/10.1155\/2018\/2379208","journal-title":"Wirel Commun Mob Comput"},{"key":"7442_CR71","doi-asserted-by":"publisher","first-page":"102177","DOI":"10.1016\/j.cose.2021.102177","volume":"103","author":"Z Wang","year":"2021","unstructured":"Wang Z, Liu Y, He D, Chan S (2021) Intrusion detection methods based on integrated deep learning model. Comput Secur 103:102177. https:\/\/doi.org\/10.1016\/j.cose.2021.102177","journal-title":"Comput Secur"},{"key":"7442_CR72","doi-asserted-by":"publisher","unstructured":"Zhang S, Cheng H, Gao J, Poon H (2022) Optimizing bi-encoder for named entity recognition via contrastive learning. arXiv preprint arXiv:2208.14565. https:\/\/doi.org\/10.48550\/arXiv.2208.14565","DOI":"10.48550\/arXiv.2208.14565"},{"issue":"4","key":"7442_CR73","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234\u20131240. https:\/\/doi.org\/10.1093\/bioinformatics\/btz682","journal-title":"Bioinformatics"},{"key":"7442_CR74","doi-asserted-by":"publisher","unstructured":"Alsentzer E, Murphy JR, Boag W, Weng WH, Jin D, Naumann T, McDermott M (2019) Publicly available clinical BERT embeddings. arxiv preprint arxiv: 1904.03323. https:\/\/doi.org\/10.48550\/arXiv.1904.03323","DOI":"10.48550\/arXiv.1904.03323"},{"key":"7442_CR75","doi-asserted-by":"crossref","unstructured":"Manning CD, Surdeanu M, Bauer J, Finkel JR, Bethard, S, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, June 23\u201324, 2014, Baltimore, Maryland USA, ACL 2014: pp 55\u201360","DOI":"10.3115\/v1\/P14-5010"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07442-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07442-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07442-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T11:02:47Z","timestamp":1748948567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07442-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":75,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["7442"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07442-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,3]]},"assertion":[{"value":"12 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declares that there is no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study does not involve both human and\/ or animal studies.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"956"}}