{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:57Z","timestamp":1740108057054,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"ational key R &D plan","award":["2019YFC0850103"],"award-info":[{"award-number":["2019YFC0850103"]}]},{"name":"the Liaoning Province Applied Basic Research Program of China","award":["No. 2022JH2\/101300250"],"award-info":[{"award-number":["No. 2022JH2\/101300250"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472169","No.62072220"],"award-info":[{"award-number":["61472169","No.62072220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Central Government Guides Local Science and Technology Development Foundation Project of Liaoning Province","award":["No.2022JH6"],"award-info":[{"award-number":["No.2022JH6"]}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2022-KF-13-06"],"award-info":[{"award-number":["2022-KF-13-06"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00521-023-08977-0","type":"journal-article","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T18:01:22Z","timestamp":1698602482000},"page":"303-321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Document-level multi-task learning approach based on coreference-aware dynamic heterogeneous graph network for event extraction"],"prefix":"10.1007","volume":"36","author":[{"given":"Ze","family":"Chen","sequence":"first","affiliation":[]},{"given":"Wanting","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Linlin","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Baoyan","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"8977_CR1","unstructured":"J H, Li S, Ji H (2021) Document-level event argument extraction by conditional generation, arXiv preprint arXiv:2104.05919"},{"key":"8977_CR2","doi-asserted-by":"crossref","unstructured":"Y C, Liu J, (2020) Event extraction as machine reading comprehension, EMNLP 1641\u20131651","DOI":"10.18653\/v1\/2020.emnlp-main.128"},{"key":"8977_CR3","doi-asserted-by":"crossref","unstructured":"W C, Zheng S, (2019) Doc2edag: An end-to-end document-level framework for chinese financial event extraction, EMNLP, pp 337\u2013346","DOI":"10.18653\/v1\/D19-1032"},{"key":"8977_CR4","doi-asserted-by":"publisher","first-page":"bba110","DOI":"10.1093\/bib\/bbaa110","volume":"23","author":"H Fei","year":"2021","unstructured":"Fei H, Ren Y (2021) Enriching contextualized language model from knowledge graph for biomedical information extraction. Brief Bioinform 23:bba110","journal-title":"Brief Bioinform"},{"key":"8977_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102473","volume":"58","author":"M Sarrouti","year":"2021","unstructured":"Sarrouti M, En-Nahnahi N (2021) Mttlade: a multi-task transfer learning-based method for adverse drug events extraction. Inform Process Manag 58:102473","journal-title":"Inform Process Manag"},{"key":"8977_CR6","doi-asserted-by":"crossref","unstructured":"O S, Tunaoglu D, (2009) Event extraction from turkish football web-casting texts using hand-crafted templates. In: IEEE international conference on semantic computing, pp 466\u2013472","DOI":"10.1109\/ICSC.2009.16"},{"key":"8977_CR7","doi-asserted-by":"crossref","unstructured":"J D, Cybulka J, (2015) Events extractor for polish based on semantics-driven extraction templates. In: language and technology conference, pp 231\u2013245","DOI":"10.1007\/978-3-319-93782-3_17"},{"key":"8977_CR8","doi-asserted-by":"crossref","unstructured":"S L, Sha L, (2015) Joint learning templates and slots for event schema induction, NAACL pp 428\u2013434","DOI":"10.18653\/v1\/N16-1049"},{"key":"8977_CR9","doi-asserted-by":"crossref","unstructured":"E M, Olubanjo T, (2016) Detecting food intake acoustic events in noisy recordings using template matching. In: IEEE-EMBS international conference on biomedical and health informatics. pp 388\u2013391","DOI":"10.1109\/BHI.2016.7455916"},{"key":"8977_CR10","unstructured":"L T, Runxin Xu, (2021) Document-level event extraction via heterogeneous graph-based interaction model with a tracker, ACL\/IJCNP, pp 3533\u20133546"},{"key":"8977_CR11","unstructured":"HangYang S D. (2021) Document-level event extraction via parallel prediction networks, ACL\/IJCNP pp 6298\u20136308"},{"key":"8977_CR12","doi-asserted-by":"crossref","unstructured":"D X. NingLuo (2021) A framework for document-level cybersecurity event extraction from open source data, CSCWD, pp 422\u2013427","DOI":"10.1109\/CSCWD49262.2021.9437745"},{"key":"8977_CR13","unstructured":"Xinya CC (2021) Grit: Generative role-filler transformers for document-level event entity extraction, EACL 634\u2013644"},{"key":"8977_CR14","unstructured":"Yaojie Liu LH (2021) Text2event: Controllable sequence-to-structure generation for end-to-end event extraction, ACL\/IJCNP pp 2795\u20132806"},{"key":"8977_CR15","unstructured":"J H. Ying Lin (2020) A joint neural model for information extraction with global features, ACL pp 5929\u20135939"},{"key":"8977_CR16","doi-asserted-by":"crossref","unstructured":"David Wadden UW (2019) Entity, relation, and event extraction with contextualized span representations, EMNLP, pp 5783\u20135788","DOI":"10.18653\/v1\/D19-1585"},{"key":"8977_CR17","doi-asserted-by":"crossref","unstructured":"e.\u00a0a. Wasi Uddin\u00a0Ahmad, Nanyun\u00a0Peng (2021) Gate: graph attention transformer encoder for cross-lingual relation and event extraction, AAAI, pp 12462\u201312470","DOI":"10.1609\/aaai.v35i14.17478"},{"key":"8977_CR18","doi-asserted-by":"crossref","unstructured":"O M, CaseLLi\u00a0T (2021) Chinese ner using lattice lstm, Proceedings of the 4th workshop on challenges and applications of automated extraction of socio-political events from text, pp 12\u201319","DOI":"10.18653\/v1\/2021.case-1.4"},{"key":"8977_CR19","doi-asserted-by":"crossref","unstructured":"Yang\u00a0HYC (2018) Dcfee: A document-level chinese financial event extraction system based on automatically labeled training data, Proceedings of ACL, pp 50\u201355","DOI":"10.18653\/v1\/P18-4009"},{"key":"8977_CR20","doi-asserted-by":"crossref","unstructured":"Liu\u00a0XH (2019) Open domain event extraction using neural latent variable models, Proceedings of ACL 2019 2860\u20132871","DOI":"10.18653\/v1\/P19-1276"},{"key":"8977_CR21","doi-asserted-by":"crossref","unstructured":"Du\u00a0XCC (2019) Document-level event role filler extraction using multi granularity contextualized encoding. Proceedings of ACL 2020, pp 2860\u20132871","DOI":"10.18653\/v1\/2020.acl-main.714"},{"key":"8977_CR22","unstructured":"Huang\u00a0RER (2012) Modeling textual cohesion for event extraction, AAAI 2012, pp 2860\u20132871"},{"key":"8977_CR23","doi-asserted-by":"crossref","unstructured":"Hang\u00a0Yang YC (2018) Dcfee: A document-level chinese financial event extraction system based on automatically labeled training data, ACL 2018, pp 50\u201355","DOI":"10.18653\/v1\/P18-4009"},{"key":"8977_CR24","doi-asserted-by":"crossref","unstructured":"Yusheng\u00a0Huang WJ (2021) Exploring sentence community for document-level event extraction, EMNLP 2021, pp 340\u2013351","DOI":"10.18653\/v1\/2021.findings-emnlp.32"},{"key":"8977_CR25","doi-asserted-by":"crossref","unstructured":"Krupka GR,(1995) Description of the sra system as used for muc-6, Proceedings of the 6th Message understanding Conference, pp 221\u2013235","DOI":"10.3115\/1072399.1072419"},{"key":"8977_CR26","first-page":"1652","volume":"60","author":"K Shaalan","year":"2009","unstructured":"Shaalan K (2009) Nera: named entity recognition for Arabic. JAm Soc 60:1652\u20131663","journal-title":"JAm Soc"},{"key":"8977_CR27","doi-asserted-by":"crossref","unstructured":"Bikel DM (1999) An algorithm that learns what\u2019s in a name, Machine learning, pp 211\u2013231","DOI":"10.1023\/A:1007558221122"},{"key":"8977_CR28","doi-asserted-by":"crossref","unstructured":"ISOZAKIH H (2002) Efficient support vector classifiers for named entity recognition, ACL 1\u20137","DOI":"10.3115\/1072228.1072282"},{"key":"8977_CR29","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert R, Weston J (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493\u20132537","journal-title":"J Mach Learn Res"},{"key":"8977_CR30","first-page":"279","volume":"8","author":"L Yao","year":"2015","unstructured":"Yao L, Liu H, Liu Y, Li X (2015) Biomedical named entity recognition based on deep neutral network. Int J Hybrid Inform Technol 8:279\u2013288","journal-title":"Int J Hybrid Inform Technol"},{"key":"8977_CR31","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang J (2018) Chinese ner using lattice lstm. In: proceedings of the 56th international conference on computational linguistics, pp 1554\u20131564","DOI":"10.18653\/v1\/P18-1144"},{"key":"8977_CR32","doi-asserted-by":"publisher","first-page":"13851","DOI":"10.1609\/aaai.v35i15.17632","volume":"35","author":"K Sun","year":"2021","unstructured":"Sun K, Zhang R, Mensah S, Mao Y, Liu X (2021) Progressive multi-task learning with controlled information flow for joint entity and relation extraction. AAAI 35:13851\u201313859","journal-title":"AAAI"},{"key":"8977_CR33","doi-asserted-by":"publisher","first-page":"9314","DOI":"10.1609\/aaai.v34i05.6471","volume":"2020","author":"CT Ya Xiao","year":"2020","unstructured":"Ya Xiao CT (2020) Joint entity and relation extraction with a hybrid transformer and reinforcement learning based model. AAAI 2020:9314\u20139321","journal-title":"AAAI"},{"issue":"3","key":"8977_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3486673","volume":"40","author":"S Yan","year":"2021","unstructured":"Yan S, Lin KJ, Zheng X, Wang H (2021) LkeRec: toward lightweight end-to-end joint representation learning for building accurate and effective recommendation. ACM Trans Inf Syst 40(3):1\u201328","journal-title":"ACM Trans Inf Syst"},{"key":"8977_CR35","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neucom.2019.09.052","volume":"374","author":"Y Wang","year":"2020","unstructured":"Wang Y, Liu H (2020) Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing 374:77\u201385","journal-title":"Neurocomputing"},{"key":"8977_CR36","unstructured":"Li\u00a0S, Ma\u00a0Q (2020) Joint-label learning by dual augmentation for time series classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp 77\u201385"},{"key":"8977_CR37","doi-asserted-by":"crossref","unstructured":"Z Z, Ma\u00a0Q (2021) Joint-label learning by dual augmentation for time series classification, AAAI 2021, pp 8847\u20138855","DOI":"10.1609\/aaai.v35i10.17071"},{"key":"8977_CR38","doi-asserted-by":"crossref","unstructured":"Zhang H, Bai J (2021) Joint coreference resolution and character linking for multiparty conversation, EACL, pp 539\u2013548","DOI":"10.18653\/v1\/2021.eacl-main.43"},{"key":"8977_CR39","doi-asserted-by":"crossref","unstructured":"Huang YJ, Kurohashi S (2021) Extractive summarization considering discourse and coreference relations based on heterogeneous graph, EACL, pp 3046\u20133052","DOI":"10.18653\/v1\/2021.eacl-main.265"},{"key":"8977_CR40","unstructured":"L P, Zeyu\u00a0Dai, Hongliang\u00a0Fei (2019) Coreference aware representation learning for neural named entity recognition. In: proceedings of the twenty-eighth international joint conference on artificial intelligence main track, pp 4946\u20134953"},{"key":"8977_CR41","doi-asserted-by":"crossref","unstructured":"T D, Zaporojets\u00a0K, Deleu\u00a0J (2021) Towards consistent document-level entity linking: Joint models for entity linking and coreference resolution","DOI":"10.18653\/v1\/2022.acl-short.88"},{"key":"8977_CR42","doi-asserted-by":"crossref","unstructured":"Q D, Xue\u00a0Z, Li\u00a0R (2022) Corefdre: Document-level relation extraction with coreference resolution","DOI":"10.1007\/978-3-031-10989-8_10"},{"key":"8977_CR43","doi-asserted-by":"crossref","unstructured":"H R, Prafulla Kumar\u00a0Choubey (2017) A sequential model for classifying temporal relations between intra-sentence events, EMNLP, pp 1796\u20131802","DOI":"10.18653\/v1\/D17-1190"},{"key":"8977_CR44","first-page":"243","volume":"27","author":"AS William","year":"1988","unstructured":"William AS, Mann C (1988) Rhetorical structure theory: toward a functional theory of text organization. Text-Interdiscip J Study Dis 27:243\u2013281","journal-title":"Text-Interdiscip J Study Dis"},{"key":"8977_CR45","unstructured":"Jason\u00a0Weston SC, Memory networks, ICIR (2015)"},{"key":"8977_CR46","unstructured":"Kendall\u00a0A, Y Gal (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482\u20137491"},{"key":"8977_CR47","unstructured":"Sha\u00a0L, Heng\u00a0J (2021) Document-level event argument extraction by conditional generation, NAACL, pp 894-908"},{"key":"8977_CR48","unstructured":"Ying\u00a0L (2020) A joint neural model for information extraction with global features, ACL, pp 7999-8009"},{"key":"8977_CR49","unstructured":"Xinya\u00a0D (2020) Event extraction by answering (almost) natural questions, EMNLP, pp 671\u2013683"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08977-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08977-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08977-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:46:01Z","timestamp":1730421961000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08977-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,29]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["8977"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08977-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,10,29]]},"assertion":[{"value":"12 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2023","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 conflict of interests in this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}