{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T03:34:09Z","timestamp":1768016049173,"version":"3.49.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key R &D Project of China","award":["No.2021QY2102"],"award-info":[{"award-number":["No.2021QY2102"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62172089"],"award-info":[{"award-number":["No. 62172089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61972087"],"award-info":[{"award-number":["No.61972087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62172090"],"award-info":[{"award-number":["No.62172090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62106045"],"award-info":[{"award-number":["No.62106045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu province","doi-asserted-by":"publisher","award":["No.BK20191258"],"award-info":[{"award-number":["No.BK20191258"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Provincial Key Laboratory of Computer Networking Technology"},{"name":"Jiangsu Provincial Key Laboratory of Network and Information Security","award":["No. BM2003201"],"award-info":[{"award-number":["No. BM2003201"]}]},{"name":"Key Laboratory of Computer Network and Information Integration of Ministry of Education of China"},{"name":"Nanjing Purple Mountain Laboratory"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00521-023-09336-9","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T18:02:28Z","timestamp":1705082548000},"page":"5715-5731","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["GAL: combining global and local contexts for interpersonal relation extraction toward document-level Chinese text"],"prefix":"10.1007","volume":"36","author":[{"given":"Jiawei","family":"Ge","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2448-6717","authenticated-orcid":false,"given":"Jiuxin","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Yingxing","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Biwei","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"9336_CR1","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1162\/tacl_a_00049","volume":"5","author":"N Peng","year":"2017","unstructured":"Peng N, Poon H, Quirk C, Toutanova K, Yih W-T (2017) Cross-sentence n-ary relation extraction with graph LSTMS. Trans Assoc Comput Linguis 5:101\u2013115","journal-title":"Trans Assoc Comput Linguis"},{"key":"9336_CR2","doi-asserted-by":"crossref","unstructured":"Miwa M, Sasaki Y (2014) Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1858\u20131869","DOI":"10.3115\/v1\/D14-1200"},{"issue":"2","key":"9336_CR3","first-page":"1","volume":"19","author":"W Che","year":"2005","unstructured":"Che W, Liu T, Li S (2005) Automatic entity relation extraction. J Chin Inf Process 19(2):1\u20136","journal-title":"J Chin Inf Process"},{"key":"9336_CR4","first-page":"1083","volume":"3","author":"D Zelenko","year":"2003","unstructured":"Zelenko D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3:1083\u20131106","journal-title":"J Mach Learn Res"},{"key":"9336_CR5","unstructured":"Plank B, Moschitti A (2013) Embedding semantic similarity in tree kernels for domain adaptation of relation extraction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1498\u20131507"},{"key":"9336_CR6","doi-asserted-by":"crossref","unstructured":"Liu C, Sun W, Chao W, Che W (2013) Convolution neural network for relation extraction. In: International Conference on Advanced Data Mining and Applications, pp. 231\u2013242. Springer","DOI":"10.1007\/978-3-642-53917-6_21"},{"key":"9336_CR7","unstructured":"Zeng D, Liu K, Lai S, Zhou G, Zhao J (2014) Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp 2335\u20132344"},{"key":"9336_CR8","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Grishman R (2015) Relation extraction: Perspective from convolutional neural networks. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp 39\u201348","DOI":"10.3115\/v1\/W15-1506"},{"key":"9336_CR9","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.1109\/TASLP.2021.3096041","volume":"29","author":"K Munir","year":"2021","unstructured":"Munir K, Zhao H, Li Z (2021) Learning context-aware convolutional filters for implicit discourse relation classification. IEEE\/ACM Trans Audio Speech Language Process 29:2421\u20132433","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"9336_CR10","unstructured":"Zhang D, Wang D (2015) Relation classification via recurrent neural network. arXiv:1508.01006"},{"key":"9336_CR11","doi-asserted-by":"crossref","unstructured":"Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (volume 2: Short Papers), pp 207\u2013212","DOI":"10.18653\/v1\/P16-2034"},{"key":"9336_CR12","doi-asserted-by":"crossref","unstructured":"Zhang Y, Qi P, Manning CD (2018) Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 2205\u20132215","DOI":"10.18653\/v1\/D18-1244"},{"key":"9336_CR13","doi-asserted-by":"crossref","unstructured":"Zhu H, Lin Y, Liu Z, Fu J, Chua T-S, Sun M (2019) Graph neural networks with generated parameters for relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 1331\u20131339","DOI":"10.18653\/v1\/P19-1128"},{"key":"9336_CR14","doi-asserted-by":"crossref","unstructured":"Fu T-J, Li P-H, Ma W-Y (2019) Graphrel: Modeling text as relational graphs for joint entity and relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 1409\u20131418","DOI":"10.18653\/v1\/P19-1136"},{"key":"9336_CR15","doi-asserted-by":"publisher","first-page":"1762","DOI":"10.1109\/TASLP.2021.3079812","volume":"29","author":"Z Xie","year":"2021","unstructured":"Xie Z, Zhu R, Liu J, Zhou G, Huang JX (2021) Hierarchical neighbor propagation with bidirectional graph attention network for relation prediction. IEEE\/ACM Trans Audio Speech Lang Process 29:1762\u20131773","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"9336_CR16","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1109\/TASLP.2021.3082295","volume":"29","author":"M Zhou","year":"2021","unstructured":"Zhou M, Ji D, Li F (2021) Relation extraction in dialogues: a deep learning model based on the generality and specialty of dialogue text. IEEE\/ACM Trans Audio Speech Lang Process 29:2015\u20132026","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"9336_CR17","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/TASLP.2022.3153256","volume":"30","author":"M Zhang","year":"2022","unstructured":"Zhang M, Qian T, Liu B (2022) Exploit feature and relation hierarchy for relation extraction. IEEE\/ACM Trans Audio Speech Lang Process 30:917\u2013930","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"9336_CR18","doi-asserted-by":"crossref","unstructured":"Yao Y, Ye D, Li P, Han X, Lin Y, Liu Z, Liu Z, Huang L, Zhou J, Sun M (2019) Docred: A large-scale document-level relation extraction dataset. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 764\u2013777","DOI":"10.18653\/v1\/P19-1074"},{"key":"9336_CR19","unstructured":"Wang H, Focke C, Sylvester R, Mishra N, Wang W (2019) Fine-tune bert for docred with two-step process. arXiv:1909.11898"},{"key":"9336_CR20","doi-asserted-by":"crossref","unstructured":"Lee S, Seo S, Oh B, Lee K-H, Shin D, Lee Y (2020) Cross-sentence n-ary relation extraction using entity link and discourse relation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 705\u2013714","DOI":"10.1145\/3340531.3412011"},{"key":"9336_CR21","doi-asserted-by":"crossref","unstructured":"Tang H, Cao Y, Zhang Z, Cao J, Fang F, Wang S, Yin P (2020) Hin: Hierarchical inference network for document-level relation extraction. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 197\u2013209. Springer","DOI":"10.1007\/978-3-030-47426-3_16"},{"key":"9336_CR22","doi-asserted-by":"crossref","unstructured":"Wang D, Hu W, Cao E, Sun W (2020) Global-to-local neural networks for document-level relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 3711\u20133721","DOI":"10.18653\/v1\/2020.emnlp-main.303"},{"key":"9336_CR23","doi-asserted-by":"crossref","unstructured":"Zeng S, Xu R, Chang B, Li L (2020) Double graph based reasoning for document-level relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1630\u20131640","DOI":"10.18653\/v1\/2020.emnlp-main.127"},{"key":"9336_CR24","doi-asserted-by":"crossref","unstructured":"Zhou W, Huang K, Ma T, Huang J (2021) Document-level relation extraction with adaptive thresholding and localized context pooling. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pp 14612\u201314620. AAAI Press, ???. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17717","DOI":"10.1609\/aaai.v35i16.17717"},{"key":"9336_CR25","doi-asserted-by":"crossref","unstructured":"Xu W, Chen K, Zhao T (2021) Discriminative reasoning for document-level relation extraction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp 1653\u20131663","DOI":"10.18653\/v1\/2021.findings-acl.144"},{"key":"9336_CR26","doi-asserted-by":"publisher","unstructured":"Peng X, Zhang C, Xu K (2022) Document-level relation extraction via subgraph reasoning. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pp. 4331\u20134337. ijcai.org, ???. https:\/\/doi.org\/10.24963\/ijcai.2022\/601. https:\/\/doi.org\/10.24963\/ijcai.2022\/601","DOI":"10.24963\/ijcai.2022\/601"},{"key":"9336_CR27","doi-asserted-by":"crossref","unstructured":"Mintz M, Bills S, Snow R, Jurafsky D (2009) Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp 1003\u20131011","DOI":"10.3115\/1690219.1690287"},{"key":"9336_CR28","unstructured":"Surdeanu M, Tibshirani J, Nallapati R, Manning CD (2012) Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp 455\u2013465"},{"key":"9336_CR29","doi-asserted-by":"crossref","unstructured":"Li S, Zhao Z, Hu R, Li W, Liu T, Du X (2018) Analogical reasoning on Chinese morphological and semantic relations. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 138\u2013143","DOI":"10.18653\/v1\/P18-2023"},{"key":"9336_CR30","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1480\u20131489","DOI":"10.18653\/v1\/N16-1174"},{"key":"9336_CR31","doi-asserted-by":"crossref","unstructured":"Wang H, He Z, Ma J, Chen W, Zhang M (2019) Ipre: a dataset for inter-personal relationship extraction. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp 103\u2013115. Springer","DOI":"10.1007\/978-3-030-32236-6_9"},{"issue":"8","key":"9336_CR32","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural computation 9(8):1735\u20131780","journal-title":"Neural computation"},{"key":"9336_CR33","doi-asserted-by":"crossref","unstructured":"Cai R, Zhang X, Wang H (2016) Bidirectional recurrent convolutional neural network for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 756\u2013765","DOI":"10.18653\/v1\/P16-1072"},{"key":"9336_CR34","doi-asserted-by":"crossref","unstructured":"Sorokin D, Gurevych I (2017) Context-aware representations for knowledge base relation extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 1784\u20131789","DOI":"10.18653\/v1\/D17-1188"},{"key":"9336_CR35","doi-asserted-by":"crossref","unstructured":"Cui Y, Che W, Liu T, Qin B, Wang S, Hu G (2020) Revisiting pre-trained models for Chinese natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp 657\u2013668. Association for Computational Linguistics, Online. https:\/\/www.aclweb.org\/anthology\/2020.findings-emnlp.58","DOI":"10.18653\/v1\/2020.findings-emnlp.58"},{"key":"9336_CR36","doi-asserted-by":"crossref","unstructured":"Huang Q, Zhu S, Feng Y, Ye Y, Lai Y, Zhao D (2021) Three sentences are all you need: Local path enhanced document 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 (Volume 2: Short Papers), pp 998\u20131004","DOI":"10.18653\/v1\/2021.acl-short.126"},{"key":"9336_CR37","doi-asserted-by":"crossref","unstructured":"Li J, Sun Y, Johnson RJ, Sciaky D, Wei C-H, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu, Z (2016) Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database 2016","DOI":"10.1093\/database\/baw068"},{"key":"9336_CR38","doi-asserted-by":"crossref","unstructured":"Wu Y, Luo R, Leung H, Ting H-F, Lam T-W (2019) Renet: A deep learning approach for extracting gene-disease associations from literature. In: International Conference on Research in Computational Molecular Biology, pp 272\u2013284. Springer","DOI":"10.1007\/978-3-030-17083-7_17"}],"updated-by":[{"DOI":"10.1007\/s00521-024-09570-9","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09336-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09336-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09336-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T09:04:56Z","timestamp":1710579896000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09336-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":38,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9336"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09336-9","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00521-024-09570-9","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]},"assertion":[{"value":"27 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised to correct the second listed affiliation.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2024","order":7,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":8,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":9,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-024-09570-9","URL":"https:\/\/doi.org\/10.1007\/s00521-024-09570-9","order":10,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}