{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T21:57:38Z","timestamp":1767650258927,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2021YFB3900601"],"award-info":[{"award-number":["2021YFB3900601"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2008085QF305"],"award-info":[{"award-number":["2008085QF305"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10489-024-05448-4","type":"journal-article","created":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T02:37:57Z","timestamp":1713667077000},"page":"5353-5372","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Graph neural networks with selective attention and path reasoning for document-level relation extraction"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9302-0358","authenticated-orcid":false,"given":"Tingting","family":"Hang","sequence":"first","affiliation":[]},{"given":"Jun","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Yunfeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Le","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,20]]},"reference":[{"key":"5448_CR1","doi-asserted-by":"publisher","unstructured":"Distiawan B, Weikum G, Qi J, Zhang R (2019) Neural relation extraction for knowledge base enrichment. In: Proceedings of the 57th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1023","DOI":"10.18653\/v1\/p19-1023"},{"key":"5448_CR2","doi-asserted-by":"publisher","unstructured":"Yu M, Yin W, Hasan KS, dos Santos C, Xiang B, Zhou B (2017) Improved neural relation detection for knowledge base question answering. In: Proceedings of the 55th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/P17-1053","DOI":"10.18653\/v1\/P17-1053"},{"key":"5448_CR3","doi-asserted-by":"publisher","unstructured":"Lai T, Cheng L, Wang D, Ye H, Zhang W (2022) Rman: Relational multi-head attention neural network for joint extraction of entities and relations. Appl Intell 52(3):3132\u20133142. https:\/\/doi.org\/10.1007\/s10489-021-02600-2","DOI":"10.1007\/s10489-021-02600-2"},{"key":"5448_CR4","doi-asserted-by":"publisher","unstructured":"Li X, Li Y, Yang J, Liu H, Hu P (2022) A relation aware embedding mechanism for relation extraction. Appl Intell, pp 1\u201310. https:\/\/doi.org\/10.1007\/s10489-021-02699-3","DOI":"10.1007\/s10489-021-02699-3"},{"key":"5448_CR5","doi-asserted-by":"publisher","unstructured":"Christopoulou F, Miwa M, Ananiadou S (2018) A walk-based model on entity graphs for relation extraction. In: Proceedings of the 56th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/P18-2014","DOI":"10.18653\/v1\/P18-2014"},{"key":"5448_CR6","doi-asserted-by":"publisher","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 conference of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1128","DOI":"10.18653\/v1\/p19-1128"},{"key":"5448_CR7","doi-asserted-by":"publisher","unstructured":"Wang H, Qin K, Lu G, Luo G, Liu G (2020) Direction-sensitive relation extraction using bi-sdp attention model. Knowl Based Syst, pp 105928. https:\/\/doi.org\/10.1016\/j.knosys.2020.105928","DOI":"10.1016\/j.knosys.2020.105928"},{"key":"5448_CR8","doi-asserted-by":"publisher","unstructured":"Hang T, Feng J, Wu Y, Yan L, Wang Y (2021) Joint extraction of entities and overlapping relations using source-target entity labeling. Expert Syst Appl 177:114853. https:\/\/doi.org\/10.1016\/j.eswa.2021.114853","DOI":"10.1016\/j.eswa.2021.114853"},{"key":"5448_CR9","doi-asserted-by":"publisher","unstructured":"Hang T, Feng J, Yan L, Wang Y, Lu J (2022) Joint extraction of entities and relations using multi-label tagging and relational alignment. Neural Comput Appl 34(8):6397\u20136412. https:\/\/doi.org\/10.1007\/s00521-021-06685-1","DOI":"10.1007\/s00521-021-06685-1"},{"key":"5448_CR10","unstructured":"Zeng D, Liu K, Lai S, Zhou G, Zhao J (2014) Relation classification via convolutional deep neural network. In: Proceedings of the 4th international conference on learning representations"},{"key":"5448_CR11","doi-asserted-by":"crossref","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"5448_CR12","doi-asserted-by":"publisher","unstructured":"Cai R, Zhang X, Wang H (2016) Bidirectional recurrent convolutional neural network for relation classification. https:\/\/doi.org\/10.18653\/v1\/p16-1072","DOI":"10.18653\/v1\/p16-1072"},{"key":"5448_CR13","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","DOI":"10.18653\/v1\/D17-1188"},{"key":"5448_CR14","doi-asserted-by":"publisher","unstructured":"Sahu SK, Christopoulou F, Miwa M, Ananiadou S (2019) Inter-sentence relation extraction with document-level graph convolutional neural network. In: Proceedings of the 57th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1423","DOI":"10.18653\/v1\/p19-1423"},{"key":"5448_CR15","doi-asserted-by":"publisher","unstructured":"Christopoulou F, Miwa M, Ananiadou S (2019) Connecting the dots: document-level neural relation extraction with edge-oriented graphs. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing. https:\/\/doi.org\/10.18653\/v1\/D19-1498","DOI":"10.18653\/v1\/D19-1498"},{"key":"5448_CR16","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.303","DOI":"10.18653\/v1\/2020.emnlp-main.303"},{"key":"5448_CR17","doi-asserted-by":"publisher","unstructured":"Guo Z, Zhang Y, Lu W (2019) Attention guided graph convolutional networks for relation extraction. In: Proceedings of the 57th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1024","DOI":"10.18653\/v1\/p19-1024"},{"key":"5448_CR18","doi-asserted-by":"publisher","unstructured":"Nan G, Guo Z, Sekulic I, Lu W (2020) Reasoning with latent structure refinement for document-Level relation extraction. In: Proceedings of the 58th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.141","DOI":"10.18653\/v1\/2020.acl-main.141"},{"key":"5448_CR19","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.127","DOI":"10.18653\/v1\/2020.emnlp-main.127"},{"key":"5448_CR20","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1007\/978-3-030-47426-3_16","DOI":"10.1007\/978-3-030-47426-3_16"},{"key":"5448_CR21","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: Proceedings of the 35th AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v35i16.17717"},{"key":"5448_CR22","doi-asserted-by":"crossref","unstructured":"Xu B, Wang Q, Lyu Y, Zhu Y, Mao Z (2021) Entity structure within and throughout: modeling mention dependencies for document-level relation extraction. In: Proceedings of the 35th AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v35i16.17665"},{"key":"5448_CR23","doi-asserted-by":"publisher","unstructured":"Zhang N, Chen X, Xie X, Deng S, Tan C, Chen M, Huang F, Si L, Chen H, Center HI (2021) Document-level relation extraction as semantic segmentation. In: Proceedings of the 30th international joint conference on artificial intelligence. https:\/\/doi.org\/10.24963\/ijcai.2021\/5517","DOI":"10.24963\/ijcai.2021\/5517"},{"key":"5448_CR24","doi-asserted-by":"publisher","unstructured":"Xiao Y, Zhang Z, Mao Y, Yang C, Han, J (2022) SAIS: supervising and augmenting intermediate steps for document-level relation extraction. https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.171","DOI":"10.18653\/v1\/2022.naacl-main.171"},{"key":"5448_CR25","doi-asserted-by":"publisher","unstructured":"Quirk C, Poon H (2017) Distant supervision for relation extraction beyond the sentence boundary. In: Proceedings of the 15th conference of the european chapter of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/e17-1110","DOI":"10.18653\/v1\/e17-1110"},{"key":"5448_CR26","doi-asserted-by":"publisher","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 Linguistics 5:101\u2013115. https:\/\/doi.org\/10.1162\/tacl_a_00049","DOI":"10.1162\/tacl_a_00049"},{"key":"5448_CR27","doi-asserted-by":"publisher","unstructured":"Song L, Zhang Y, Wang Z, Gildea D (2018) N-ary relation extraction using graph state LSTMs. In: Proceedings of the 2018 conference on empirical methods in natural language processing. https:\/\/doi.org\/10.18653\/v1\/d18-1246","DOI":"10.18653\/v1\/d18-1246"},{"key":"5448_CR28","doi-asserted-by":"publisher","unstructured":"Zhang Z, Yu B, Shu X, Liu T, Tang H, Yubin W, Guo L (2020) Document-level relation extraction with dual-tier heterogeneous graph. In: Proceedings of the 28th international conference on computational linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.143","DOI":"10.18653\/v1\/2020.coling-main.143"},{"key":"5448_CR29","doi-asserted-by":"publisher","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing. https:\/\/doi.org\/10.3115\/v1\/d14-1162","DOI":"10.3115\/v1\/d14-1162"},{"key":"5448_CR30","doi-asserted-by":"publisher","unstructured":"Chiu B, Crichton G, Korhonen A, Pyysalo S (2016) How to train good word embeddings for biomedical NLP. In: Proceedings of the 15th workshop on biomedical natural language processing. https:\/\/doi.org\/10.18653\/v1\/W16-2922","DOI":"10.18653\/v1\/W16-2922"},{"key":"5448_CR31","unstructured":"Li Y, Tarlow D, Brockschmidt M, Zemel R (2016) Gated graph sequence neural networks. In: Proceedings of the 4th international conference on learning representations"},{"key":"5448_CR32","doi-asserted-by":"publisher","unstructured":"Song L, Zhang Y, Wang Z, Gildea D (2018) A graph-to-sequence model for amr-to-text generation. In: Proceedings of the 56th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/P18-1150","DOI":"10.18653\/v1\/P18-1150"},{"key":"5448_CR33","doi-asserted-by":"publisher","unstructured":"Zhang Y, Liu Q, Song L (2018) Sentence-state LSTM for text representation. In: Proceedings of the 56th annual meeting of the association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/P18-1030","DOI":"10.18653\/v1\/P18-1030"},{"key":"5448_CR34","doi-asserted-by":"publisher","unstructured":"Jia R, Wong C, Poon H (2019) Document-Level N-ary relation extraction with multiscale representation learning. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies. https:\/\/doi.org\/10.18653\/v1\/n19-1370","DOI":"10.18653\/v1\/n19-1370"},{"key":"5448_CR35","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.18653\/v1\/p19-1074","DOI":"10.18653\/v1\/p19-1074"},{"key":"5448_CR36","doi-asserted-by":"publisher","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 J Biol Databases Curation 2016. https:\/\/doi.org\/10.1093\/database\/baw068","DOI":"10.1093\/database\/baw068"},{"key":"5448_CR37","doi-asserted-by":"publisher","unstructured":"Wu Y, Luo R, Leung HC, Ting H-F, Lam T-W (2019) Renet: A deep learning approach for extracting gene-disease associations from literature. In: Proceedings of the 23rd international conference on research in computational molecular biology. https:\/\/doi.org\/10.1007\/978-3-030-17083-7_17","DOI":"10.1007\/978-3-030-17083-7_17"},{"key":"5448_CR38","doi-asserted-by":"publisher","unstructured":"Tan Q, He R, Bing L, Ng HT (2022) Document-level relation extraction with adaptive focal loss and knowledge distillation. In: Findings of the association for computational linguistics: ACL 2022. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.132","DOI":"10.18653\/v1\/2022.findings-acl.132"},{"key":"5448_CR39","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch. In: Proceedings of NIPS 2017 workshop"},{"key":"5448_CR40","unstructured":"Kingma DP, Ba J(2015) Adam: a method for stochastic optimization. In: Proceedings of the 3th international conference on learning representations"},{"key":"5448_CR41","doi-asserted-by":"crossref","unstructured":"Caruana R, Lawrence S, Giles CL (2000) Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Proceedings of the advances in neural information processing systems 13","DOI":"10.1109\/IJCNN.2000.857823"},{"key":"5448_CR42","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, 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. https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"5448_CR43","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv:1907.11692"},{"key":"5448_CR44","doi-asserted-by":"publisher","unstructured":"Beltagy I, Lo K, Cohan A (2019) SciBERT: A pretrained language model for scientific text. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing . https:\/\/doi.org\/10.18653\/v1\/D19-1371","DOI":"10.18653\/v1\/D19-1371"},{"key":"5448_CR45","doi-asserted-by":"crossref","unstructured":"Huang K, Wang G, Ma T, Huang J (2020) Entity and evidence guided relation extraction for docred. arXiv:2008.12283","DOI":"10.18653\/v1\/2021.repl4nlp-1.30"},{"key":"5448_CR46","unstructured":"Xie Y, Shen J, Li S, Mao Y, Han J (2021) Eider: Evidence-enhanced document-level relation extraction. arXiv:2106.08657"},{"key":"5448_CR47","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.136","DOI":"10.18653\/v1\/2020.coling-main.136"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05448-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05448-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05448-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T14:10:48Z","timestamp":1715609448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05448-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":47,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5448"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05448-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"7 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2024","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 declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}