{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T05:13:27Z","timestamp":1778303607258,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"International Scientific and Technological Cooperation Project of Huangpu and Development Districts in Guangzhou","award":["No. 2023GH05"],"award-info":[{"award-number":["No. 2023GH05"]}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["No. 2023A1515011370"],"award-info":[{"award-number":["No. 2023A1515011370"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Characteristic Innovation Projects of Guangdong Colleges and Universities","award":["No. 2018KTSCX049"],"award-info":[{"award-number":["No. 2018KTSCX049"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 32371114"],"award-info":[{"award-number":["No. 32371114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangdong Provincial Key Laboratory","award":["No. 2023B1212060076"],"award-info":[{"award-number":["No. 2023B1212060076"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13042-026-03004-9","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:12:50Z","timestamp":1772280770000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PE-DER: a dynamic entity-relation interaction method based on multi-prompts enhancement for continual few-shot relation extraction"],"prefix":"10.1007","volume":"17","author":[{"given":"Weibo","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zehong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fenghuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengbo","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"issue":"7","key":"3004_CR1","first-page":"3366","volume":"44","author":"M De Lange","year":"2021","unstructured":"De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, Slabaugh G, Tuytelaars T (2021) A continual learning survey: defying forgetting in classification tasks. IEEE Trans Pattern Anal Mach Intell 44(7):3366\u20133385","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3004_CR2","doi-asserted-by":"crossref","unstructured":"Wang P, Song Y, Liu T, Lin B, Cao Y, Li S, Sui Z (2022) Learning robust representations for continual relation extraction via adversarial class augmentation. In: Proceedings of the 2022 Conference on empirical methods in natural language processing, pp 6264\u20136278","DOI":"10.18653\/v1\/2022.emnlp-main.420"},{"key":"3004_CR3","doi-asserted-by":"crossref","unstructured":"Zhao W, Cui Y, Hu W (2023) Improving continual relation extraction by distinguishing analogous semantics. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (vol 1: Long Papers), pp 1162\u20131175","DOI":"10.18653\/v1\/2023.acl-long.65"},{"key":"3004_CR4","doi-asserted-by":"crossref","unstructured":"Qin C, Joty S (2022) Continual few-shot relation learning via embedding space regularization and data augmentation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (vol 1: Long Papers), pp 2776\u20132789","DOI":"10.18653\/v1\/2022.acl-long.198"},{"key":"3004_CR5","doi-asserted-by":"crossref","unstructured":"Chen X, Wu H, Shi X (2023) Consistent prototype learning for few-shot continual relation extraction. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (vol 1: Long Papers), pp 7409\u20137422","DOI":"10.18653\/v1\/2023.acl-long.409"},{"issue":"12","key":"3004_CR6","doi-asserted-by":"publisher","first-page":"8267","DOI":"10.1109\/TKDE.2024.3419117","volume":"36","author":"L Zhang","year":"2024","unstructured":"Zhang L, Li Y, Wang Q, Wang Y, Yan H, Wang J, Liu J (2024) Fprompt-plm: flexible-prompt on pretrained language model for continual few-shot relation extraction. IEEE Trans Knowl Data Eng 36(12):8267\u20138282","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3004_CR7","doi-asserted-by":"crossref","unstructured":"Wang X, Wang Z, Hu W (2023) Serial contrastive knowledge distillation for continual few-shot relation extraction. In: Findings of the Association for Computational Linguistics: ACL 2023, pp 12693\u201312706","DOI":"10.18653\/v1\/2023.findings-acl.804"},{"issue":"4","key":"3004_CR8","doi-asserted-by":"publisher","first-page":"3516","DOI":"10.1007\/s10489-024-05327-y","volume":"54","author":"Z Wei","year":"2024","unstructured":"Wei Z, Zhang Y, Lian B, Fan Y, Zhao J (2024) Joint data augmentation and knowledge distillation for few-shot continual relation extraction. Appl Intell 54(4):3516\u20133528","journal-title":"Appl Intell"},{"key":"3004_CR9","doi-asserted-by":"crossref","unstructured":"Ma S, Han J, Liang Y, Cheng B (2024) Making pre-trained language models better continual few-shot relation extractors. In: Proceedings of the 2024 Joint International Conference on computational linguistics, language resources and evaluation (LREC-COLING 2024), pp 10970\u201310983","DOI":"10.63317\/3ezkbexw5upj"},{"key":"3004_CR10","doi-asserted-by":"crossref","unstructured":"Xu Z, Wang C, Qiu M, Luo F, Xu R, Huang S, Huang J (2023) Making pre-trained language models end-to-end few-shot learners with contrastive prompt tuning. In: Proceedings of the sixteenth acm International Conference on web search and data mining, pp 438\u2013446","DOI":"10.1145\/3539597.3570398"},{"key":"3004_CR11","doi-asserted-by":"crossref","unstructured":"Park KH, Song K, Park GM (2024) Pre-trained vision and language transformers are few-shot incremental learners. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 23881\u201323890","DOI":"10.1109\/CVPR52733.2024.02254"},{"key":"3004_CR12","doi-asserted-by":"crossref","unstructured":"Zhong Z, Chen D (2021) A frustratingly easy approach for entity and relation extraction. In: Proceedings of the 2021 Conference of the North American chapter of the association for computational linguistics: human language technologies, pp 50\u201361","DOI":"10.18653\/v1\/2021.naacl-main.5"},{"key":"3004_CR13","first-page":"2778","volume":"2022","author":"X Chen","year":"2022","unstructured":"Chen X, Zhang N, Xie X, Deng S, Yao Y, Tan C, Huang F, Si L, Chen H (2022) Knowprompt: knowledge-aware prompt-tuning with synergistic optimization for relation extraction. Proc ACM Web Conf 2022:2778\u20132788","journal-title":"Proc ACM Web Conf"},{"key":"3004_CR14","doi-asserted-by":"crossref","unstructured":"Zhang Z, Mai W, Xiong H, Wu C, Xue Y (2023) A token-wise graph-based framework for multimodal named entity recognition. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp 2153\u20132158","DOI":"10.1109\/ICME55011.2023.00368"},{"key":"3004_CR15","doi-asserted-by":"publisher","first-page":"2801","DOI":"10.1109\/TASLP.2022.3199655","volume":"30","author":"H Zhang","year":"2022","unstructured":"Zhang H, Liang B, Yang M, Wang H, Xu R (2022) Prompt-based prototypical framework for continual relation extraction. IEEE\/ACM Trans Audio Speech Lang Process 30:2801\u20132813","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"3004_CR16","doi-asserted-by":"crossref","unstructured":"Zhou W, Zhang S, Naumann T, Chen M, Poon H (2023) Continual contrastive finetuning improves low-resource relation extraction. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (vol 1: Long Papers), pp 13249\u201313263","DOI":"10.18653\/v1\/2023.acl-long.739"},{"key":"3004_CR17","doi-asserted-by":"crossref","unstructured":"Huang M, Xiao M, Wang L, Du Y (2024) Dp-cre: continual relation extraction via decoupled contrastive learning and memory structure preservation. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp 5338\u20135349","DOI":"10.63317\/46z8asaxr6c4"},{"key":"3004_CR18","doi-asserted-by":"crossref","unstructured":"Cui L, Yang D, Yu J, Hu C, Cheng J, Yi J, Xiao Y (2021) Refining sample embeddings with relation prototypes to enhance continual relation extraction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (vol 1: Long Papers), pp 232\u2013243","DOI":"10.18653\/v1\/2021.acl-long.20"},{"key":"3004_CR19","doi-asserted-by":"crossref","unstructured":"Zhao K, Xu H, Yang J, Gao K (2022) Consistent representation learning for continual relation extraction. Findings of the Association for Computational Linguistics: ACL 2022:3402\u20133411","DOI":"10.18653\/v1\/2022.findings-acl.268"},{"key":"3004_CR20","doi-asserted-by":"crossref","unstructured":"Le TT, Nguyen M, Nguyen TT, Van LN, Nguyen TH (2024) Continual relation extraction via sequential multi-task learning. In: Proceedings of the AAAI Conference on artificial intelligence, pp 18444\u201318452","DOI":"10.1609\/aaai.v38i16.29805"},{"key":"3004_CR21","doi-asserted-by":"crossref","unstructured":"Wan Z, Cheng F, Mao Z, Liu Q, Song H, Li J, Kurohashi S (2023) Gpt-re: in-context learning for relation extraction using large language models. In: Proceedings of the 2023 Conference on empirical methods in natural language processing, pp 3534\u20133547","DOI":"10.18653\/v1\/2023.emnlp-main.214"},{"key":"3004_CR22","doi-asserted-by":"crossref","unstructured":"Zhou Y, Ren T, Zhu C, Sun X, Liu J, Ding X, Xu M, Ji R (2021) Trar: routing the attention spans in transformer for visual question answering. In: Proceedings of the IEEE\/CVF International Conference on computer vision, pp 2074\u20132084","DOI":"10.1109\/ICCV48922.2021.00208"},{"key":"3004_CR23","doi-asserted-by":"crossref","unstructured":"Han X, Zhu H, Yu P, Wang Z, Yao Y, Liu Z, Sun M (2018) FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Proceedings of the 2018 Conference on empirical methods in natural language processing, pp 4803\u20134809","DOI":"10.18653\/v1\/D18-1514"},{"key":"3004_CR24","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhong V, Chen D, Angeli G, Manning CD (2017) Position-aware attention and supervised data improve slot filling. In: Conference on empirical methods in natural language processing","DOI":"10.18653\/v1\/D17-1004"},{"key":"3004_CR25","doi-asserted-by":"crossref","unstructured":"Tran Q, Thanh N, Anh N, Hai N, Le T, Ngo L, Nguyen T (2024) Preserving generalization of language models in few-shot continual relation extraction. In: Proceedings of the 2024 Conference on empirical methods in natural language processing, pp 13771\u201313784","DOI":"10.18653\/v1\/2024.emnlp-main.763"},{"key":"3004_CR26","doi-asserted-by":"crossref","unstructured":"Thanh NX, Le AD, Tran Q, Le TT, Van LN, Nguyen TH (2025) Few-shot, no problem: descriptive continual relation extraction. In: Proceedings of the AAAI Conference on artificial intelligence, pp 25282\u201325290","DOI":"10.1609\/aaai.v39i24.34715"},{"key":"3004_CR27","unstructured":"Chaudhry A, Ranzato M, Rohrbach M, Elhoseiny M (2018) Efficient lifelong learning with a-gem. In: International Conference on learning representations"},{"key":"3004_CR28","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (long and short papers), pp 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"3004_CR29","unstructured":"Loshchilov I (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"},{"key":"3004_CR30","unstructured":"Touvron H, Martin L, Stone K, et\u00a0al (2023) Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03004-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03004-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03004-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T04:54:34Z","timestamp":1778302474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03004-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["3004"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03004-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]},"assertion":[{"value":"24 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2026","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":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"158"}}