{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:01:48Z","timestamp":1778799708384,"version":"3.51.4"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62572089"],"award-info":[{"award-number":["62572089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114596","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:11:36Z","timestamp":1774357896000},"page":"114596","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Multi-stage reasoning framework for biomedical document-level relation extraction with dynamic memory mechanism"],"prefix":"10.1016","volume":"174","author":[{"given":"Xinyuan","family":"Sun","sequence":"first","affiliation":[]},{"given":"Jianyuan","family":"Yuan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0771-9742","authenticated-orcid":false,"given":"Jinzhong","family":"Ning","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5843-4675","authenticated-orcid":false,"given":"Yijia","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114596_b1","series-title":"Gpt-4 technical report","author":"Achiam","year":"2023"},{"key":"10.1016\/j.engappai.2026.114596_b2","doi-asserted-by":"crossref","unstructured":"Barraco, M., Sarto, S., Cornia, M., Baraldi, L., Cucchiara, R., 2023. With a little help from your own past: prototypical memory networks for image captioning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3021\u20133031.","DOI":"10.1109\/ICCV51070.2023.00282"},{"key":"10.1016\/j.engappai.2026.114596_b3","series-title":"SciBERT: A pretrained language model for scientific text","author":"Beltagy","year":"2019"},{"key":"10.1016\/j.engappai.2026.114596_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112777","article-title":"EMGE: Entities and mentions gradual enhancement with semantics and connection modelling for document-level relation extraction","volume":"309","author":"Chen","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114596_b5","series-title":"Proceedings of the BioCreative VI Workshop","first-page":"102","article-title":"Document triage and relation extraction for protein-protein interactions affected by mutations","author":"Chen","year":"2017"},{"key":"10.1016\/j.engappai.2026.114596_b6","doi-asserted-by":"crossref","unstructured":"Chen, L., Su, J., Lam, T.-W., Luo, R., 2023. Exploring pair-aware triangular attention for biomedical relation extraction. In: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. pp. 1\u20135.","DOI":"10.1145\/3584371.3612994"},{"key":"10.1016\/j.engappai.2026.114596_b7","doi-asserted-by":"crossref","first-page":"bay066","DOI":"10.1093\/database\/bay066","article-title":"Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings","volume":"2018","author":"Corbett","year":"2018","journal-title":"Database"},{"key":"10.1016\/j.engappai.2026.114596_b8","series-title":"An end-to-end model for entity-level relation extraction using multi-instance learning","author":"Eberts","year":"2021"},{"key":"10.1016\/j.engappai.2026.114596_b9","series-title":"A sequence-to-sequence approach for document-level relation extraction","author":"Giorgi","year":"2022"},{"key":"10.1016\/j.engappai.2026.114596_b10","series-title":"Asian Conference on Machine Learning","first-page":"231","article-title":"Understanding more knowledge makes the transformer perform better in document-level relation extraction","author":"Haotian","year":"2024"},{"key":"10.1016\/j.engappai.2026.114596_b11","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W., 2019. Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 603\u2013612.","DOI":"10.1109\/ICCV.2019.00069"},{"issue":"1","key":"10.1016\/j.engappai.2026.114596_b12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3597610","article-title":"Document-level relation extraction via separate relation representation and logical reasoning","volume":"42","author":"Huang","year":"2023","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.engappai.2026.114596_b13","series-title":"Document-level N-ary relation extraction with multiscale representation learning","author":"Jia","year":"2019"},{"key":"10.1016\/j.engappai.2026.114596_b14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-3517-7","article-title":"Broad-coverage biomedical relation extraction with SemRep","volume":"21","author":"Kilicoglu","year":"2020","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"10.1016\/j.engappai.2026.114596_b15","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.engappai.2026.114596_b16","article-title":"Document-level biomedical relation extraction via knowledge-enhanced graph and dynamic generative adversarial networks","author":"Li","year":"2025","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"10.1016\/j.engappai.2026.114596_b17","unstructured":"Li, L., Lian, R., Lu, H., Tang, J., 2022. Document-level biomedical relation extraction based on multi-dimensional fusion information and multi-granularity logical reasoning. In: Proceedings of the 29th International Conference on Computational Linguistics. pp. 2098\u20132107."},{"key":"10.1016\/j.engappai.2026.114596_b18","series-title":"2020 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"1740","article-title":"KEoG: a knowledge-aware edge-oriented graph neural network for document-level relation extraction","author":"Li","year":"2020"},{"key":"10.1016\/j.engappai.2026.114596_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2024.104756","article-title":"Biomedical document-level relation extraction with thematic capture and localized entity pooling","volume":"160","author":"Li","year":"2024","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.engappai.2026.114596_b20","article-title":"BioCreative V CDR task corpus: a resource for chemical disease relation extraction","volume":"2016","author":"Li","year":"2016","journal-title":"Database"},{"issue":"Suppl 7","key":"10.1016\/j.engappai.2026.114596_b21","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1186\/s12911-021-01733-1","article-title":"Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge","volume":"21","author":"Li","year":"2021","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"10.1016\/j.engappai.2026.114596_b22","series-title":"Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021","first-page":"1359","article-title":"MRN: A locally and globally mention-based reasoning network for document-level relation extraction","author":"Li","year":"2021"},{"key":"10.1016\/j.engappai.2026.114596_b23","doi-asserted-by":"crossref","unstructured":"Li, B., Ye, W., Sheng, Z., Xie, R., Xi, X., Zhang, S., 2020. Graph enhanced dual attention network for document-level relation extraction. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 1551\u20131560.","DOI":"10.18653\/v1\/2020.coling-main.136"},{"issue":"7","key":"10.1016\/j.engappai.2026.114596_b24","doi-asserted-by":"crossref","first-page":"6852","DOI":"10.1109\/TKDE.2022.3177226","article-title":"Distantly-supervised long-tailed relation extraction using constraint graphs","volume":"35","author":"Liang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"10.1016\/j.engappai.2026.114596_b25","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0190926","article-title":"Drug drug interaction extraction from the literature using a recursive neural network","volume":"13","author":"Lim","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.engappai.2026.114596_b26","article-title":"M 3 D: a multimodal, multilingual and multitask dataset for grounded document-level information extraction","author":"Liu","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.engappai.2026.114596_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102718","article-title":"Multi-granularity sequential neural network for document-level biomedical relation extraction","volume":"58","author":"Liu","year":"2021","journal-title":"Inf. Process. Manage."},{"key":"10.1016\/j.engappai.2026.114596_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126099","article-title":"Document-level relation extraction with structural encoding and entity-pair-level information interaction","volume":"268","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114596_b29","series-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017"},{"key":"10.1016\/j.engappai.2026.114596_b30","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baw039","article-title":"Efficient chemical-disease identification and relationship extraction using wikipedia to improve recall","volume":"2016","author":"Lowe","year":"2016","journal-title":"Database"},{"key":"10.1016\/j.engappai.2026.114596_b31","doi-asserted-by":"crossref","first-page":"bay138","DOI":"10.1093\/database\/bay138","article-title":"Extracting chemical\u2013protein interactions from literature using sentence structure analysis and feature engineering","volume":"2019","author":"Lung","year":"2019","journal-title":"Database"},{"key":"10.1016\/j.engappai.2026.114596_b32","series-title":"SENT: Sentence-level distant relation extraction via negative training","author":"Ma","year":"2021"},{"key":"10.1016\/j.engappai.2026.114596_b33","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.procs.2020.09.080","article-title":"Combining multi-task learning with transfer learning for biomedical named entity recognition","volume":"176","author":"Mehmood","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.engappai.2026.114596_b34","series-title":"Reasoning with latent structure refinement for document-level relation extraction","author":"Nan","year":"2020"},{"key":"10.1016\/j.engappai.2026.114596_b35","series-title":"IJCAI","first-page":"4331","article-title":"Document-level relation extraction via subgraph reasoning.","author":"Peng","year":"2022"},{"key":"10.1016\/j.engappai.2026.114596_b36","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3389\/fcell.2020.00673","article-title":"Named entity recognition and relation detection for biomedical information extraction","volume":"8","author":"Perera","year":"2020","journal-title":"Front. Cell Dev. Biol."},{"key":"10.1016\/j.engappai.2026.114596_b37","series-title":"Inter-sentence relation extraction with document-level graph convolutional neural network","author":"Sahu","year":"2019"},{"key":"10.1016\/j.engappai.2026.114596_b38","series-title":"Label verbalization and entailment for effective zero-and few-shot relation extraction","author":"Sainz","year":"2021"},{"key":"10.1016\/j.engappai.2026.114596_b39","series-title":"Biomedical relation extraction via adaptive document-relation cross-mapping and concept unique identifier","author":"Shang","year":"2025"},{"issue":"18","key":"10.1016\/j.engappai.2026.114596_b40","doi-asserted-by":"crossref","first-page":"15429","DOI":"10.1007\/s00521-022-07223-3","article-title":"Enhanced graph convolutional network based on node importance for document-level relation extraction","volume":"34","author":"Sun","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.engappai.2026.114596_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110428","article-title":"Document-level relation extraction with two-stage dynamic graph attention networks","volume":"267","author":"Sun","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114596_b42","series-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"10.1016\/j.engappai.2026.114596_b43","doi-asserted-by":"crossref","unstructured":"Tran, H.M., Nguyen, M.T., Nguyen, T.H., 2020. The dots have their values: exploiting the node-edge connections in graph-based neural models for document-level relation extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 4561\u20134567.","DOI":"10.18653\/v1\/2020.findings-emnlp.409"},{"key":"10.1016\/j.engappai.2026.114596_b44","series-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"10.1016\/j.engappai.2026.114596_b45","series-title":"Global-to-local neural networks for document-level relation extraction","author":"Wang","year":"2020"},{"key":"10.1016\/j.engappai.2026.114596_b46","series-title":"Entity-centered cross-document relation extraction","author":"Wang","year":"2022"},{"key":"10.1016\/j.engappai.2026.114596_b47","doi-asserted-by":"crossref","unstructured":"Wei, Y., Li, Q., 2022. SagDRE: Sequence-aware graph-based document-level relation extraction with adaptive margin loss. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 2000\u20132008.","DOI":"10.1145\/3534678.3539304"},{"key":"10.1016\/j.engappai.2026.114596_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126615","article-title":"A document-level relation extraction method based on dual-angle attention transfer fusion","author":"Wei","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114596_b49","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al., 2020. Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. pp. 38\u201345.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"10.1016\/j.engappai.2026.114596_b50","series-title":"Research in Computational Molecular Biology: 23rd Annual International Conference, RECOMB 2019, Washington, DC, USA, May 5-8, 2019, Proceedings 23","first-page":"272","article-title":"Renet: A deep learning approach for extracting gene-disease associations from literature","author":"Wu","year":"2019"},{"key":"10.1016\/j.engappai.2026.114596_b51","series-title":"An efficient memory-augmented transformer for knowledge-intensive nlp tasks","author":"Wu","year":"2022"},{"key":"10.1016\/j.engappai.2026.114596_b52","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Jin, Y., Hao, K., 2024. Federated document-level biomedical relation extraction with localized context contrast. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). pp. 7163\u20137173.","DOI":"10.63317\/4rr6f2k4bz3i"},{"key":"10.1016\/j.engappai.2026.114596_b53","series-title":"Denoising relation extraction from document-level distant supervision","author":"Xiao","year":"2020"},{"key":"10.1016\/j.engappai.2026.114596_b54","series-title":"SAIS: supervising and augmenting intermediate steps for document-level relation extraction","author":"Xiao","year":"2021"},{"key":"10.1016\/j.engappai.2026.114596_b55","series-title":"2019 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"755","article-title":"Extracting drug-drug interactions with a dependency-based graph convolution neural network","author":"Xiong","year":"2019"},{"key":"10.1016\/j.engappai.2026.114596_b56","first-page":"14167","article-title":"Document-level relation extraction with reconstruction","volume":"vol. 35","author":"Xu","year":"2021"},{"key":"10.1016\/j.engappai.2026.114596_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.csl.2023.101574","article-title":"Document-level relation extraction with entity mentions deep attention","volume":"84","author":"Xu","year":"2024","journal-title":"Comput. Speech Lang."},{"key":"10.1016\/j.engappai.2026.114596_b58","first-page":"14149","article-title":"Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction","volume":"vo. 35","author":"Xu","year":"2021"},{"key":"10.1016\/j.engappai.2026.114596_b59","series-title":"Autore: Document-level relation extraction with large language models","author":"Xue","year":"2024"},{"key":"10.1016\/j.engappai.2026.114596_b60","series-title":"A survey on recent advances in named entity recognition from deep learning models","author":"Yadav","year":"2019"},{"key":"10.1016\/j.engappai.2026.114596_b61","series-title":"Coreferential reasoning learning for language representation","author":"Ye","year":"2020"},{"key":"10.1016\/j.engappai.2026.114596_b62","series-title":"Double graph based reasoning for document-level relation extraction","author":"Zeng","year":"2020"},{"key":"10.1016\/j.engappai.2026.114596_b63","doi-asserted-by":"crossref","first-page":"3659","DOI":"10.1109\/TASLP.2023.3316454","article-title":"Document-level relation extraction with context guided mention integration and inter-pair reasoning","volume":"31","author":"Zeng","year":"2023","journal-title":"IEEE\/ACM Trans. Audio, Speech, Lang. Process."},{"key":"10.1016\/j.engappai.2026.114596_b64","doi-asserted-by":"crossref","unstructured":"Zhang, R., Li, Y., Zou, L., 2023. A novel table-to-graph generation approach for document-level joint entity and relation extraction. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 10853\u201310865.","DOI":"10.18653\/v1\/2023.acl-long.607"},{"key":"10.1016\/j.engappai.2026.114596_b65","first-page":"13967","article-title":"Exploring self-distillation based relational reasoning training for document-level relation extraction","volume":"vol. 37","author":"Zhang","year":"2023"},{"key":"10.1016\/j.engappai.2026.114596_b66","unstructured":"Zhang, K., Wu, P., Yu, B., Wu, K., Zheng, A., Huang, X., Zhu, C., Peng, M., Zan, H., Song, Y., 2025. CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning. In: Proceedings of the 31st International Conference on Computational Linguistics. pp. 8272\u20138284."},{"key":"10.1016\/j.engappai.2026.114596_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2023.104459","article-title":"Biomedical document relation extraction with prompt learning and KNN","volume":"145","author":"Zhao","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.engappai.2026.114596_b68","series-title":"Training language models with memory augmentation","author":"Zhong","year":"2022"},{"key":"10.1016\/j.engappai.2026.114596_b69","first-page":"14612","article-title":"Document-level relation extraction with adaptive thresholding and localized context pooling","volume":"vol. 35","author":"Zhou","year":"2021"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008778?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008778?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:59:11Z","timestamp":1776160751000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626008778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":69,"alternative-id":["S0952197626008778"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114596","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-stage reasoning framework for biomedical document-level relation extraction with dynamic memory mechanism","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114596","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114596"}}