{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:14:23Z","timestamp":1769721263357,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819783663","type":"print"},{"value":"9789819783670","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-8367-0_11","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T11:56:09Z","timestamp":1732794969000},"page":"174-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Joint Entity and\u00a0Relation Extraction Based on\u00a0Bidirectional Update and\u00a0Long-Term Memory Gate Mechanism"],"prefix":"10.1007","author":[{"given":"Yili","family":"Qian","sequence":"first","affiliation":[]},{"given":"Enlong","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Haonan","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Cai, R., Zhang, X., Wang, H.: Bidirectional recurrent convolutional neural network for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 756\u2013765 (2016)","DOI":"10.18653\/v1\/P16-1072"},{"key":"11_CR2","unstructured":"Chan, Y.S., Roth, D.: 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 (2011)"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"3504","DOI":"10.1109\/TASLP.2021.3124365","volume":"29","author":"Y Cui","year":"2021","unstructured":"Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z.: Pre-training with whole word masking for Chinese BERT. IEEE\/ACM Trans. Audio, Speech, Lang. Process. 29, 3504\u20133514 (2021)","journal-title":"IEEE\/ACM Trans. Audio, Speech, Lang. Process."},{"issue":"7","key":"11_CR4","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s13042-021-01491-6","volume":"13","author":"C Gao","year":"2022","unstructured":"Gao, C., Zhang, X., Liu, H., Yun, W., Jiang, J.: A joint extraction model of entities and relations based on relation decomposition. Int. J. Mach. Learn. Cybern. 13(7), 1833\u20131845 (2022)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"116951","DOI":"10.1016\/j.eswa.2022.116951","volume":"200","author":"S Jia","year":"2022","unstructured":"Jia, S., Shijia, E., Ding, L., Chen, X., Xiang, Y.: Hybrid neural tagging model for open relation extraction. Expert Syst. Appl. 200, 116951 (2022)","journal-title":"Expert Syst. Appl."},{"key":"11_CR6","unstructured":"Kong, W., Xia, Y.: CARE: co-attention network for joint entity and relation extraction. arXiv preprint arXiv:2308.12531 (2023)"},{"key":"11_CR7","doi-asserted-by":"publisher","unstructured":"Li, H., Islam, M.T., Huangliang, K., Chen, Z., Zhao, K., Zhang, H.: SETFF: a semantic enhanced table filling framework for joint entity and relation extraction. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds.) Pacific Rim International Conference on Artificial Intelligence, pp. 169\u2013182. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20865-2_13","DOI":"10.1007\/978-3-031-20865-2_13"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"Li, L., Wang, Z., Qin, X., Lu, H.: Dual interactive attention network for joint entity and relation extraction. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds.) CCF International Conference on Natural Language Processing and Chinese Computing, pp. 259\u2013271. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17120-8_21","DOI":"10.1007\/978-3-031-17120-8_21"},{"issue":"12","key":"11_CR9","first-page":"23","volume":"43","author":"Z Li","year":"2022","unstructured":"Li, Z., Chen, M., Ma, Z., et al.: CMedCausal: Chinese medical causal relationship extraction dataset. J. Med. Inform. 43(12), 23\u201327 (2022)","journal-title":"J. Med. Inform."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Li, Z., Fu, L.: A relation-aware span-level transformer network for joint entity and relation extraction. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892140"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Liu, P., Du, J., Shao, Y., Guan, Z.: Relation extraction model based on semantic enhancement mechanism. In: 2023 5th International Conference on Data-Driven Optimization of Complex Systems (DOCS), pp.\u00a01\u20137. IEEE (2023)","DOI":"10.1109\/DOCS60977.2023.10294482"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Liu, X., Cai, R., Zhang, Y.: A 2D entity pair tagging scheme for relation triplet extraction. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds.) Knowledge Science, Engineering and Management. KSEM 2023. LNCS, vol. 14119, pp. 16\u201329. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-40289-0_2","DOI":"10.1007\/978-3-031-40289-0_2"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Hu, J., Wan, X., Chang, T.H.: A simple yet effective relation information guided approach for few-shot relation extraction. arXiv preprint arXiv:2205.09536 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.62"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"9789","DOI":"10.1109\/ACCESS.2022.3232493","volume":"11","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wen, F., Zong, T., Li, T.: Research on joint extraction method of entity and relation triples based on hierarchical cascade labeling. IEEE Access 11, 9789\u20139798 (2022)","journal-title":"IEEE Access"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., Jurafsky, D.: 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 (2009)","DOI":"10.3115\/1690219.1690287"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. arXiv preprint arXiv:1601.00770 (2016)","DOI":"10.18653\/v1\/P16-1105"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Miwa, M., Sasaki, Y.: 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 (2014)","DOI":"10.3115\/v1\/D14-1200"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Nayak, T., Ng, H.T.: Effective modeling of encoder-decoder architecture for joint entity and relation extraction. In: Proceedings of the AAAI Conference on Artificial Intelligence, No. 05, pp. 8528\u20138535 (2020)","DOI":"10.1609\/aaai.v34i05.6374"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Ning, J., Yang, Z., Sun, Y., Wang, Z., Lin, H.: OD-RTE: a one-stage object detection framework for relational triple extraction. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 11120\u201311135 (2023)","DOI":"10.18653\/v1\/2023.acl-long.623"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Ren, F., et al.: A novel global feature-oriented relational triple extraction model based on table filling. arXiv preprint arXiv:2109.06705 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.208"},{"key":"11_CR21","doi-asserted-by":"publisher","unstructured":"Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balcazar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, 20\u201324 September 2010, Proceedings, Part III 21, pp. 148\u2013163. Springer, Cham (2010). https:\/\/doi.org\/10.1007\/978-3-642-15939-8_10","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Shang, Y.M., Huang, H., Mao, X.: OneRel: joint entity and relation extraction with one module in one step. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 11285\u201311293 (2022)","DOI":"10.1609\/aaai.v36i10.21379"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Stanovsky, G., Michael, J., Zettlemoyer, L., Dagan, I.: Supervised open information extraction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 885\u2013895 (2018)","DOI":"10.18653\/v1\/N18-1081"},{"key":"11_CR24","unstructured":"Su, J.: GPLinker: entity-relation joint extraction based on GlobalPointer (2022)"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Takanobu, R., Zhang, T., Liu, J., Huang, M.: A hierarchical framework for relation extraction with reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 7072\u20137079 (2019)","DOI":"10.1609\/aaai.v33i01.33017072"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Tang, W., et al.: UniRel: unified representation and interaction for joint relational triple extraction. arXiv preprint arXiv:2211.09039 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.477"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, W.: Two are better than one: joint entity and relation extraction with table-sequence encoders. arXiv preprint arXiv:2010.03851 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.133"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yu, B., Zhang, Y., Liu, T., Zhu, H., Sun, L.: TPLinker: single-stage joint extraction of entities and relations through token pair linking. arXiv preprint arXiv:2010.13415 (2020)","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Wei, Z., Su, J., Wang, Y., Tian, Y., Chang, Y.: A novel cascade binary tagging framework for relational triple extraction. arXiv preprint arXiv:1909.03227 (2019)","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"11_CR30","doi-asserted-by":"publisher","unstructured":"Xu, W., Yin, S., Zhao, J., Pu, T.: Deep semantic fusion representation based on special mechanism of information transmission for joint entity-relation extraction. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds.) PRICAI 2021: Trends in Artificial Intelligence: 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, 8\u201312 November 2021, Proceedings, Part II 18, pp. 73\u201385. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-89363-7_6","DOI":"10.1007\/978-3-030-89363-7_6"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Zhou, X., Pan, S., Zhu, Q., Song, Z., Guo, L.: A relation-specific attention network for joint entity and relation extraction. In: International Joint Conference on Artificial Intelligence (2021)","DOI":"10.24963\/ijcai.2020\/561"},{"key":"11_CR32","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, The 25th International Conference on Computational Linguistics: Technical Papers, pp. 2335\u20132344 (2014)"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Zeng, D., Zhang, H., Liu, Q.: CopyMTL: copy mechanism for joint extraction of entities and relations with multi-task learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 9507\u20139514 (2020)","DOI":"10.1609\/aaai.v34i05.6495"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Zeng, X., He, S., Zeng, D., Liu, K., Liu, S., Zhao, J.: 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 (2019)","DOI":"10.18653\/v1\/D19-1035"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Zeng, X., Zeng, D., He, S., Liu, K., Zhao, J.: 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 (vol. 1: Long Papers), pp. 506\u2013514 (2018)","DOI":"10.18653\/v1\/P18-1047"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, Y., Fu, G.: End-to-end neural relation extraction with global optimization. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1730\u20131740 (2017)","DOI":"10.18653\/v1\/D17-1182"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, R.H., et al.: Minimize exposure bias of Seq2Seq models in joint entity and relation extraction. arXiv preprint arXiv:2009.07503 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.23"},{"key":"11_CR38","doi-asserted-by":"publisher","first-page":"106888","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.: Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction. Knowl.-Based Syst. 219, 106888 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Zheng, H., et al.: PRGC: potential relation and global correspondence based joint relational triple extraction. arXiv preprint arXiv:2106.09895 (2021)","DOI":"10.18653\/v1\/2021.acl-long.486"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. arXiv preprint arXiv:1706.05075 (2017)","DOI":"10.18653\/v1\/P17-1113"},{"key":"11_CR41","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Chen, D.: A frustratingly easy approach for entity and relation extraction. arXiv preprint arXiv:2010.12812 (2020)","DOI":"10.18653\/v1\/2021.naacl-main.5"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8367-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T12:06:00Z","timestamp":1732795560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8367-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9789819783663","9789819783670"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8367-0_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cips-cl.org\/static\/CCL2024\/en\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}