{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:42:11Z","timestamp":1742913731409,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031171192"},{"type":"electronic","value":"9783031171208"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17120-8_13","type":"book-chapter","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:02:58Z","timestamp":1663938178000},"page":"159-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BART-Reader: Predicting Relations Between Entities via\u00a0Reading Their Document-Level Context Information"],"prefix":"10.1007","author":[{"given":"Hang","family":"Yan","sequence":"first","affiliation":[]},{"given":"Yu","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Junqi","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Xiangkun","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Qipeng","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Xipeng","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Xuanjing","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Christopoulou, F., Miwa, M., Ananiadou, S.: Connecting the dots: document-level neural relation extraction with edge-oriented graphs. In: EMNLP-IJCNLP (2019)","DOI":"10.18653\/v1\/D19-1498"},{"key":"13_CR2","unstructured":"Dai, D., Ren, J., Zeng, S., Chang, B., Sui, Z.: Coarse-to-fine entity representations for document-level relation extraction. CoRR abs\/2012.02507 (2020)"},{"key":"13_CR3","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (2019)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Huang, Q., Zhu, S., Feng, Y., Ye, Y., Lai, Y., Zhao, D.: Three sentences are all you need: local path enhanced document relation extraction. In: ACL\/IJCNLP (2021)","DOI":"10.18653\/v1\/2021.acl-short.126"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"13_CR6","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)","DOI":"10.1093\/database\/baw068"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Li, S., Ji, H., Han, J.: Document-level event argument extraction by conditional generation. CoRR abs\/2104.05919 (2021). https:\/\/arxiv.org\/abs\/2104.05919","DOI":"10.18653\/v1\/2021.naacl-main.69"},{"key":"13_CR8","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019). http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"13_CR9","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)"},{"key":"13_CR10","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":"13_CR11","unstructured":"Paolini, G., et al.: Structured prediction as translation between augmented natural languages. In: ICLR (2021)"},{"key":"13_CR12","unstructured":"Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., Huang, X.: Pre-trained models for natural language processing: a survey. CoRR abs\/2003.08271 (2020)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Sun, C., Qiu, X., Xu, Y., Huang, X.: How to fine-tune BERT for text classification? In: CCL (2019)","DOI":"10.1007\/978-3-030-32381-3_16"},{"key":"13_CR14","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of NeuIPS (2017)"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Wu, T., Li, X., et al.: Curriculum-meta learning for order-robust continual relation extraction. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i12.17241"},{"key":"13_CR16","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)","DOI":"10.1007\/978-3-030-17083-7_17"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Xu, B., Wang, Q., Lyu, Y., Zhu, Y., Mao, Z.: Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i16.17665"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Xu, W., Chen, K., Zhao, T.: Document-level relation extraction with reconstruction. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i16.17667"},{"key":"13_CR19","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":"13_CR20","doi-asserted-by":"crossref","unstructured":"Ye, D., Lin, Y., Du, J., Liu, Z., Sun, M., Liu, Z.: Coreferential reasoning learning for language representation. arXiv:2004.06870 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.582"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Ye, H., et al.: Contrastive triple extraction with generative transformer. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i16.17677"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Zeng, S., Wu, Y., Chang, B.: SIRE: separate intra- and inter-sentential reasoning for document-level relation extraction. In: Findings of ACL\/IJCNLP (2021)","DOI":"10.18653\/v1\/2021.findings-acl.47"},{"key":"13_CR23","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 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.127"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, N., et al.: Document-level relation extraction as semantic segmentation. In: IJCAI (2021)","DOI":"10.24963\/ijcai.2021\/551"},{"key":"13_CR25","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":"13_CR26","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"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17120-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:05:11Z","timestamp":1663938311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17120-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171192","9783031171208"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17120-8_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"327","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":"73","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":"22% - 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":"3","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":"1.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}