{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:30:11Z","timestamp":1781217011977,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981779","type":"print"},{"value":"9789819981786","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8178-6_33","type":"book-chapter","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T10:02:54Z","timestamp":1701252174000},"page":"427-440","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Document-Level Relation Extraction with\u00a0Relation Correlation Enhancement"],"prefix":"10.1007","author":[{"given":"Yusheng","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhouhan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"33_CR1","unstructured":"Baruch, E.B., et al.: Asymmetric loss for multi-label classification. CoRR (2020)"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Cai, R., Zhang, X., Wang, H.: Bidirectional recurrent convolutional neural network for relation classification. In: Proceedings of ACL (2016)","DOI":"10.18653\/v1\/P16-1072"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Che, X., Chen, D., Mi, J.: Label correlation in multi-label classification using local attribute reductions with fuzzy rough sets. In: FSS (2022)","DOI":"10.1016\/j.fss.2021.03.016"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Chen, M., Lan, G., Du, F., Lobanov, V.S.: Joint learning with pre-trained transformer on named entity recognition and relation extraction tasks for clinical analytics. In: ClinicalNLP@EMNLP 2020, Online, November 19, 2020 (2020)","DOI":"10.18653\/v1\/2020.clinicalnlp-1.26"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wei, X., Wang, P., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00532"},{"key":"33_CR6","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of ACL (2019)"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Feng, J., Huang, M., Zhao, L., Yang, Y., Zhu, X.: Reinforcement learning for relation classification from noisy data. In: Proceedings of AAAI (2018)","DOI":"10.1609\/aaai.v32i1.12063"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Guo, Z., Zhang, Y., Lu, W.: Attention guided graph convolutional networks for relation extraction. In: Proceedings of ACL (2019)","DOI":"10.18653\/v1\/P19-1024"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"He, H., Balakrishnan, A., Eric, M., Liang, P.: Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings. In: Proceedings of ACL (2017)","DOI":"10.18653\/v1\/P17-1162"},{"key":"33_CR10","unstructured":"Hendrickx, I., et al.: Semeval-2010 task 8. In: SEW@NAACL-HLT 2009, Boulder, CO, USA, June 4, 2009 (2009)"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Hixon, B., Clark, P., Hajishirzi, H.: Learning knowledge graphs for question answering through conversational dialog. In: ACL (2015)","DOI":"10.3115\/v1\/N15-1086"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Li, B., Ye, W., Sheng, Z., Xie, R., Xi, X., Zhang, S.: Graph enhanced dual attention network for document-level relation extraction. In: Proceedings of COLING (2020)","DOI":"10.18653\/v1\/2020.coling-main.136"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Biocreative V CDR task corpus: a resource for chemical disease relation extraction. Database J. Biol. Databases Curation 2016 (2016)","DOI":"10.1093\/database\/baw068"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Li, J., Xu, K., Li, F., Fei, H., Ren, Y., Ji, D.: MRN: a locally and globally mention-based reasoning network for document-level relation extraction. In: Proceedings of ACL (2021)","DOI":"10.18653\/v1\/2021.findings-acl.117"},{"key":"33_CR15","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR (2019)"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Nan, G., Guo, Z., Sekulic, I., Lu, W.: Reasoning with latent structure refinement for document-level relation extraction. In: ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.141"},{"key":"33_CR17","unstructured":"Peng, N., Poon, H., Quirk, C., Toutanova, K., Yih, W.: Cross-sentence N-ary relation extraction with graph LSTMs. In: TACL (2017)"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: EMNLP, pp. 1532\u20131543. ACL (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"dos Santos, C.N., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: ACL (2015)","DOI":"10.3115\/v1\/P15-1061"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Tang, H., et al.: HIN: hierarchical inference network for document-level relation extraction. In: Proceedings of KDD (2020)","DOI":"10.1007\/978-3-030-47426-3_16"},{"key":"33_CR21","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)"},{"key":"33_CR22","unstructured":"Wang, H., Focke, C., Sylvester, R., Mishra, N., Wang, W.Y.: Fine-tune BERT for docred with two-step process. CoRR (2019)"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Wang, L., Cao, Z., de Melo, G., Liu, Z.: Relation classification via multi-level attention CNNs. In: Proceedings of ACL (2016)","DOI":"10.18653\/v1\/P16-1123"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Multi-label classification with label graph superimposing. In: Proceedings of AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6909"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Y., Luo, R., Leung, H.C.M., Ting, H., Lam, T.W.: RENET: a deep learning approach for extracting gene-disease associations from literature. In: RECOMB 2019, Washington, DC, USA, May 5\u20138, 2019, Proceedings (2019)","DOI":"10.1007\/978-3-030-17083-7_17"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Tan, C., Fan, Z., Xu, Q., Zhu, W.: Joint entity and relation extraction with a hybrid transformer and reinforcement learning based model. In: Proceedings of AAAI (2020)","DOI":"10.1609\/aaai.v34i05.6471"},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Yao, Y., et al.: Docred: a large-scale document-level relation extraction dataset. In: ACL (2019)","DOI":"10.18653\/v1\/P19-1074"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Ye, D., et al.: Coreferential reasoning learning for language representation. In: Proceedings of EMNLP (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.582"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Ye, Z., Ling, Z.: Distant supervision relation extraction with intra-bag and inter-bag attentions. In: Proceedings of ACL (2019)","DOI":"10.18653\/v1\/N19-1288"},{"key":"33_CR30","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING (2014)"},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Zeng, S., Xu, R., Chang, B., Li, L.: Double graph based reasoning for document-level relation extraction. In: EMNLP (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.127"},{"key":"33_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, N., et al.: Document-level relation extraction as semantic segmentation. In: Proceedings of IJCAI (2021)","DOI":"10.24963\/ijcai.2021\/551"},{"key":"33_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Proceedings of EMNLP (2017)","DOI":"10.18653\/v1\/D17-1004"},{"key":"33_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Document-level relation extraction with dual-tier heterogeneous graph. In: Proceedings of COLING (2020)","DOI":"10.18653\/v1\/2020.coling-main.143"},{"key":"33_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, H., Xu, Y., Yao, W., Liu, Z., Lang, C., Jiang, H.: Global context-enhanced graph convolutional networks for document-level relation extraction. In: COLING (2020)","DOI":"10.18653\/v1\/2020.coling-main.461"},{"key":"33_CR36","doi-asserted-by":"crossref","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open (2020)","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"33_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of ACL (2016)","DOI":"10.18653\/v1\/P16-2034"},{"key":"33_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, W., Huang, K., Ma, T., Huang, J.: Document-level relation extraction with adaptive thresholding and localized context pooling. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i16.17717"},{"key":"33_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, H., Lin, Y., Liu, Z., Fu, J., Chua, T., Sun, M.: Graph neural networks with generated parameters for relation extraction. In: Proceedings of ACL (2019)","DOI":"10.18653\/v1\/P19-1128"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8178-6_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:33:33Z","timestamp":1709829213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8178-6_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9789819981779","9789819981786"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8178-6_33","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"30 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}