{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:35:28Z","timestamp":1784291728143,"version":"3.55.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Extracting relational triples from a piece of text is an essential task in knowledge graph construction. However, most existing methods either identify entities before predicting their relations, or detect relations before recognizing associated entities. This order may lead to error accumulation because once there is an error in the initial step, it will accumulate to subsequent steps. To solve this problem, we propose a parallel model for jointly extracting entities and relations, called PRE-Span, which consists of two mutually independent submodules. Specifically, candidate entities and relations are first generated by enumerating token sequences in sentences. Then, two independent submodules (Entity Extraction Module and Relation Detection Module) are designed to predict entities and relations. Finally, the predicted results of the two submodules are analyzed to select entities and relations, which are jointly decoded to obtain relational triples. The advantage of this method is that all triples can be extracted in just one step. Extensive experiments on the WebNLG*, NYT*, NYT and WebNLG datasets show that our model outperforms other baselines at 94.4%, 88.3%, 86.5% and 83.0%, respectively.<\/jats:p>","DOI":"10.1007\/s11063-024-11616-x","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T19:01:36Z","timestamp":1715108496000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Parallel Model for Jointly Extracting Entities and Relations"],"prefix":"10.1007","volume":"56","author":[{"given":"Zuqin","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujie","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jike","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wencheng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zining","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"11616_CR1","doi-asserted-by":"crossref","unstructured":"Dai Quoc Nguyen TDN, Nguyen DQ, Phung D (2018) A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of NAACL-HLT, pp. 327\u2013333 (2018)","DOI":"10.18653\/v1\/N18-2053"},{"key":"11616_CR2","doi-asserted-by":"crossref","unstructured":"Hu S, Zou L, Zhang X (2018) A state-transition framework to answer complex questions over knowledge base. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 2098\u20132108","DOI":"10.18653\/v1\/D18-1234"},{"key":"11616_CR3","unstructured":"Zelenko D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3(Feb):1083\u20131106"},{"key":"11616_CR4","unstructured":"Chan YS, Roth D (2011) Exploiting syntactico-semantic structures for relation extraction. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 551\u2013560"},{"key":"11616_CR5","unstructured":"Yu X, Lam W (2010) Jointly identifying entities and extracting relations in encyclopedia text via a graphical model approach. In: Coling 2010: Posters, pp 1399\u20131407"},{"key":"11616_CR6","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"},{"key":"11616_CR7","doi-asserted-by":"crossref","unstructured":"Sun C, Gong Y, Wu Y, Gong M, Jiang D, Lan M, Sun S, Duan N (2019) Joint type inference on entities and relations via graph convolutional networks. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1361\u20131370","DOI":"10.18653\/v1\/P19-1131"},{"key":"11616_CR8","doi-asserted-by":"crossref","unstructured":"Nguyen D, Nguyen TD, Nguyen DQ, Phumg Q-D(2018) A novel embedding model for knowledge base completion based on convolutional neutral network","DOI":"10.18653\/v1\/N18-2053"},{"key":"11616_CR9","doi-asserted-by":"publisher","unstructured":"Ren F, Zhang L, Yin S, Zhao X, Liu, S, Li B, Liu Y (2021) A novel global feature-oriented relational triple extraction model based on table filling. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 2646\u20132656. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.208. https:\/\/aclanthology.org\/2021.emnlp-main.208","DOI":"10.18653\/v1\/2021.emnlp-main.208"},{"key":"11616_CR10","doi-asserted-by":"crossref","unstructured":"Li X, Luo X, Dong C, Yang D, Luan B, He Z (2021) TDEER: an efficient translating decoding schema for joint extraction of entities and relations. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 8055\u20138064","DOI":"10.18653\/v1\/2021.emnlp-main.635"},{"key":"11616_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106888","volume":"219","author":"K Zhao","year":"2021","unstructured":"Zhao K, Xu H, Cheng Y, Li X, Gao K (2021) Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction. Knowl-Based Syst 219:106888","journal-title":"Knowl-Based Syst"},{"key":"11616_CR12","doi-asserted-by":"crossref","unstructured":"Li Z, Fu L, Wang X, Zhang H, Zhou C (2022) RFBFN: a relation-first blank filling network for joint relational triple extraction. In: Proceedings of the 60th annual meeting of the association for computational linguistics: student research workshop, pp 10\u201320","DOI":"10.18653\/v1\/2022.acl-srw.2"},{"key":"11616_CR13","doi-asserted-by":"crossref","unstructured":"Shang YM, Huang H, Sun X, Wei W, Mao XL (2022) Relational triple extraction: one step is enough","DOI":"10.24963\/ijcai.2022\/605"},{"key":"11616_CR14","doi-asserted-by":"publisher","unstructured":"Zheng S, Wang F, Bao H, Hao Y, Zhou P, Xu B (2017) Joint extraction of entities and relations based on a novel tagging scheme. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vol 1, Association for Computational Linguistics, Vancouver, Canada, https:\/\/doi.org\/10.18653\/v1\/P17-1113. https:\/\/aclanthology.org\/P17-1113 1227\u20131236.pp","DOI":"10.18653\/v1\/P17-1113"},{"key":"11616_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103384","volume":"103","author":"L Luo","year":"2020","unstructured":"Luo L, Yang Z, Cao M, Wang L, Zhang Y, Lin H (2020) A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature. J Biomed Inform 103:103384","journal-title":"J Biomed Inform"},{"key":"11616_CR16","doi-asserted-by":"crossref","unstructured":"Zheng H, Wen R, Chen X, Yang Y, Zhang Y, Zhang Z, Zhang N, Qin B, Ming X, Zheng Y (2021) PRGC: potential relation and global correspondence based joint relational triple 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 1: Long Papers), pp 6225\u20136235","DOI":"10.18653\/v1\/2021.acl-long.486"},{"issue":"2","key":"11616_CR17","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1007\/s11063-021-10690-9","volume":"54","author":"Q Wang","year":"2022","unstructured":"Wang Q, Zhang Q, Zuo M, He S, Zhang B (2022) A entity relation extraction model with enhanced position attention in food domain. Neural Process Lett 54(2):1449\u20131464","journal-title":"Neural Process Lett"},{"key":"11616_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2023.3265851","author":"Z Wang","year":"2023","unstructured":"Wang Z, Nie H, Zheng W, Wang Y, Li X (2023) A novel tensor learning model for joint relational triplet extraction. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2023.3265851","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"11616_CR19","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/app13020842","volume":"13","author":"B Jiang","year":"2023","unstructured":"Jiang B, Cao J (2023) Joint extraction of entities and relations via entity and relation heterogeneous graph attention networks. Appl Sci 13(2):842","journal-title":"Appl Sci"},{"key":"11616_CR20","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":"11616_CR21","doi-asserted-by":"publisher","unstructured":"Wang Y, Yu B, Zhang Y, Liu T, Zhu H, Sun L (2020) TPLinker: single-stage joint extraction of entities and relations through token pair linkingIn: Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Barcelona, Spain (Online), 1572\u20131582. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.138. https:\/\/aclanthology.org\/2020.colingmain.138","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"11616_CR22","doi-asserted-by":"publisher","unstructured":"Wang Y, Sun C, Wu Y, Zhou H, Li L, Yan J. UniRE: a unified label space for entity 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, Association for Computational Linguistics, Online, 220\u2013231. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.19. https:\/\/aclanthology.org\/2021.acl-long.19","DOI":"10.18653\/v1\/2021.acl-long.19"},{"key":"11616_CR23","doi-asserted-by":"publisher","first-page":"11285","DOI":"10.1609\/aaai.v36i10.21379","volume":"36","author":"Y-M Shang","year":"2022","unstructured":"Shang Y-M, Huang H, Mao X (2022) OneRel: joint entity and relation extraction with one module in one step. Proceedings of the AAAI conference on artificial intelligence 36:11285\u201311293","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"11616_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110550","volume":"271","author":"C Gao","year":"2023","unstructured":"Gao C, Zhang X, Li L, Li J, Zhu R, Du K, Ma Q (2023) ERGM: a multi-stage joint entity and relation extraction with global entity match. Knowl-Based Syst 271:110550","journal-title":"Knowl-Based Syst"},{"key":"11616_CR25","unstructured":"Wang Y, Sun C, Wu Y, Li L, Yan J, Zhou H (2023) HIORE: leveraging high-order interactions for unified entity relation extraction. arXiv preprint arXiv:2305.04297"},{"key":"11616_CR26","doi-asserted-by":"crossref","unstructured":"Zeng X, Zeng D, He S, Liu K, Zhao J (2018) Extracting relational facts by an end-to-end neural model with copy mechanism. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 506\u2013514","DOI":"10.18653\/v1\/P18-1047"},{"key":"11616_CR27","doi-asserted-by":"publisher","first-page":"9507","DOI":"10.1609\/aaai.v34i05.6495","volume":"34","author":"D Zeng","year":"2020","unstructured":"Zeng D, Zhang H, Liu Q (2020) CopyMTL: copy mechanism for joint extraction of entities and relations with multi-task learning. Proceedings of the AAAI conference on artificial intelligence 34:9507\u20139514","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"11616_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107677","volume":"236","author":"H Huang","year":"2022","unstructured":"Huang H, Shang Y-M, Sun X, Wei W, Mao X (2022) Three birds, one stone: a novel translation based framework for joint entity and relation extraction. Knowl-Based Syst 236:107677","journal-title":"Knowl-Based Syst"},{"key":"11616_CR29","doi-asserted-by":"crossref","unstructured":"Huang Z, Liang L, Zhu X, Weng H, Yan J, Hao T (2022) An improved partition filter network for entity-relation joint extraction. In: Neural computing for advanced applications: third international conference, NCAA 2022, Jinan, China, July 8\u201310, 2022, Proceedings, Part I. Springer, pp 129\u2013141","DOI":"10.1007\/978-981-19-6142-7_10"},{"key":"11616_CR30","doi-asserted-by":"crossref","unstructured":"Zhong Z, Chen D (2021) A frustratingly easy approach for entity and relation extraction. In: 2021 conference of the north American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2021. Association for Computational Linguistics (ACL), pp 50\u201361","DOI":"10.18653\/v1\/2021.naacl-main.5"},{"key":"11616_CR31","doi-asserted-by":"crossref","unstructured":"Riedel S, Yao L, McCallum A (2010) Modeling relations and their mentions without labeled text. In: Machine learning and knowledge discovery in databases: European conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III 21. Springer, pp 148\u2013163","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"11616_CR32","doi-asserted-by":"crossref","unstructured":"Gardent C, Shimorina A, Narayan S, Perez-Beltrachini L (2017) Creating training corpora for NLG micro-planning. In: 55th annual meeting of the association for computational linguistics (ACL)","DOI":"10.18653\/v1\/P17-1017"},{"key":"11616_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3264735","author":"D Sui","year":"2023","unstructured":"Sui D, Zeng X, Chen Y, Liu K, Zhao J (2023) Joint entity and relation extraction with set prediction networks. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3264735","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11616_CR34","doi-asserted-by":"publisher","unstructured":"Yan Z, Zhang C, Fu J, Zhang Q, Wei Z (2021) A partition filter network for joint entity and relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 185\u2013197. https:\/\/doi.org\/10.18653\/v1\/2021.emnlpmain.17. https:\/\/aclanthology.org\/2021.emnlp-main.17","DOI":"10.18653\/v1\/2021.emnlpmain.17"},{"key":"11616_CR35","doi-asserted-by":"crossref","unstructured":"Ren F, Zhang L, Zhao X, Yin S, Liu S, Li B (2022) A simple but effective bidirectional framework for relational triple extraction. In: Proceedings of the fifteenth ACM international conference on web search and data mining, pp 824\u2013832","DOI":"10.1145\/3488560.3498409"},{"key":"11616_CR36","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.eswa.2018.07.032","volume":"114","author":"G Bekoulis","year":"2018","unstructured":"Bekoulis G, Deleu J, Demeester T, Develder C (2018) Bert: pre-training of deep bidirectional transformers for language understanding. Expert Syst Appl 114:34\u201345","journal-title":"Expert Syst Appl"},{"key":"11616_CR37","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.eswa.2018.07.032","volume":"114","author":"G Bekoulis","year":"2018","unstructured":"Bekoulis G, Deleu J, Demeester T, Develder C (2018) Joint entity recognition and relation extraction as a multi-head selection problem. Expert Syst Appl 114:34\u201345","journal-title":"Expert Syst Appl"},{"key":"11616_CR38","doi-asserted-by":"crossref","unstructured":"Zeng X, He S, Zeng D, Liu K, Liu S, Zhao J (2019) Learning the extraction order of multiple relational facts in a sentence with reinforcement learning. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 367\u2013377","DOI":"10.18653\/v1\/D19-1035"},{"key":"11616_CR39","unstructured":"Yu B, Zhang Z, Shu X, Liu T, Wang Y, Wang B, Li S (2020) Joint extraction of entities and relations based on a novel decomposition strategy"},{"key":"11616_CR40","doi-asserted-by":"publisher","first-page":"51315","DOI":"10.1109\/ACCESS.2020.2980859","volume":"8","author":"Y Hong","year":"2020","unstructured":"Hong Y, Liu Y, Yang S, Zhang K, Wen A, Hu J (2020) Improving graph convolutional networks based on relation-aware attention for end-to-end relation extraction. IEEE Access 8:51315\u201351323","journal-title":"IEEE Access"},{"key":"11616_CR41","first-page":"4054","volume":"2020","author":"Y Yuan","year":"2020","unstructured":"Yuan Y, Zhou X, Pan S, Zhu Q, Song Z, Guo L (2020) A relation-specific attention network for joint entity and relation extraction. IJCAI 2020:4054\u20134060","journal-title":"IJCAI"},{"key":"11616_CR42","doi-asserted-by":"publisher","unstructured":"Wei Z, Su J, Wang Y, Tian Y, Chang Y (2020) A novel cascade binary tagging framework for relational triple extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, 1476\u20131488. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.136. https:\/\/aclanthology.org\/2020.acl-main.136","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"11616_CR43","doi-asserted-by":"crossref","unstructured":"Niu W, Chen Q, Zhang W, Ma J, Hu Z (2021) GCN2-NAA: two-stage graph convolutional networks with node-aware attention for joint entity and relation extraction. In: 2021 13th international conference on machine learning and computing, pp 542\u2013549","DOI":"10.1145\/3457682.3457765"},{"key":"11616_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115806","volume":"187","author":"N Zhang","year":"2022","unstructured":"Zhang N, Deng S, Ye H, Zhang W, Chen H (2022) Robust triple extraction with cascade bidirectional capsule network. Expert Syst Appl 187:115806","journal-title":"Expert Syst Appl"},{"issue":"3","key":"11616_CR45","doi-asserted-by":"publisher","first-page":"3132","DOI":"10.1007\/s10489-021-02600-2","volume":"52","author":"T Lai","year":"2022","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","journal-title":"Appl Intell"},{"issue":"2","key":"11616_CR46","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/app13020842","volume":"13","author":"B Jiang","year":"2023","unstructured":"Jiang B, Cao J (2023) Joint extraction of entities and relations via entity and relation heterogeneous graph attention networks. Appl Sci 13(2):842","journal-title":"Appl Sci"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11616-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11616-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11616-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:17:09Z","timestamp":1721042229000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11616-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,7]]},"references-count":46,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["11616"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11616-x","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,7]]},"assertion":[{"value":"6 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 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 Conflict of interest in this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"165"}}