{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T21:24:40Z","timestamp":1768425880659,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819549863","type":"print"},{"value":"9789819549870","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-4987-0_38","type":"book-chapter","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:29:33Z","timestamp":1768393773000},"page":"543-557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DCCR: Debiasing Cross-Document Event Coreference Resolution with\u00a0Counterfactual Reasoning"],"prefix":"10.1007","author":[{"given":"Long","family":"Yao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabo","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuyuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhen","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liejun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"38_CR1","unstructured":"Ahmed, S.R., et al.: Linear cross-document event coreference resolution with x-amr. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 10517\u201310529 (2024)"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Ahmed, S.R., Nath, A., Martin, J.H., Krishnaswamy, N.: 2* n is better than n2: decomposing event coreference resolution into two tractable problems. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 1569\u20131583 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.100"},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Barhom, S., Shwartz, V., Eirew, A., Bugert, M., Reimers, N., Dagan, I.: Revisiting joint modeling of cross-document entity and event coreference resolution. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4179\u20134189 (2019)","DOI":"10.18653\/v1\/P19-1409"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Caciularu, A., Cohan, A., Beltagy, I., Peters, M.E., Cattan, A., Dagan, I.: Cdlm: cross-document language modeling. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2648\u20132662 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.225"},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Cattan, A., Eirew, A., Stanovsky, G., Joshi, M., Dagan, I.: Cross-document coreference resolution over predicted mentions. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 5100\u20135107 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.453"},{"issue":"4","key":"38_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2025.104085","volume":"62","author":"X Chen","year":"2025","unstructured":"Chen, X., Li, P., Zhu, Q.: Improving cross-document event coreference resolution by discourse coherence and structure. Inform. Process. Manag. 62(4), 104085 (2025)","journal-title":"Inform. Process. Manag."},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, S., Li, P., Zhu, Q.: Cross-document event coreference resolution on discourse structure. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 4833\u20134843 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.294"},{"key":"38_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Z., Hu, L., Li, W., Shao, Y., Nie, L.: Causal intervention and counterfactual reasoning for multi-modal fake news detection. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 627\u2013638 (2023)","DOI":"10.18653\/v1\/2023.acl-long.37"},{"key":"38_CR9","unstructured":"Cybulska, A., Vossen, P.: Using a sledgehammer to crack a nut? lexical diversity and event coreference resolution. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), pp. 4545\u20134552 (2014)"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Ding, B., Min, Q., Ma, S., Li, Y., Yang, L., Zhang, Y.: A rationale-centric counterfactual data augmentation method for cross-document event coreference resolution. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 1112\u20131140 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.63"},{"key":"38_CR11","unstructured":"Gao, Q., et al.: Enhancing cross-document event coreference resolution by discourse structure and semantic information. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 5907\u20135921 (2024)"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Held, W., Iter, D., Jurafsky, D.: Focus on what matters: applying discourse coherence theory to cross document coreference. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1406\u20131417 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.106"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Imambi, S., Prakash, K.B., Kanagachidambaresan, G.: Pytorch. Programming with TensorFlow: solution for edge computing applications, pp. 87\u2013104 (2021)","DOI":"10.1007\/978-3-030-57077-4_10"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Lin, X., Wu, Z., Chen, G., Li, G., Yu, Y.: A causal debiasing framework for unsupervised salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1610\u20131619 (2022)","DOI":"10.1609\/aaai.v36i2.20052"},{"key":"38_CR15","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6-9 May 2019. OpenReview.net (2019)"},{"key":"38_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103632","volume":"303","author":"Y Lu","year":"2022","unstructured":"Lu, Y., Lin, H., Tang, J., Han, X., Sun, L.: End-to-end neural event coreference resolution. Artif. Intell. 303, 103632 (2022)","journal-title":"Artif. Intell."},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Lu, Y., et al.: Knowledge editing with dynamic knowledge graphs for multi-hop question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 24741\u201324749 (2025)","DOI":"10.1609\/aaai.v39i23.34655"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Nath, A., Avari, S.M., Chelle, A., Krishnaswamy, N.: Okay, let\u2019s do this! modeling event coreference with generated rationales and knowledge distillation. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 3931\u20133946 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.218"},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Neuberg, L.G.: Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000. Econometric Theory 19(4), 675\u2013685 (2003)","DOI":"10.1017\/S0266466603004109"},{"key":"38_CR20","unstructured":"Pearl, J., Glymour, M., Jewell, N.P.: Causal inference in statistics: a primer. John Wiley & Sons (2016)"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Perot, V., et\u00a0al.: Lmdx: language model-based document information extraction and localization. In: Findings of the Association for Computational Linguistics ACL 2024, pp. 15140\u201315168 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.899"},{"key":"38_CR22","doi-asserted-by":"crossref","unstructured":"Qian, C., Feng, F., Wen, L., Ma, C., Xie, P.: Counterfactual inference for text classification debiasing. 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. 5434\u20135445 (2021)","DOI":"10.18653\/v1\/2021.acl-long.422"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Tang, K., Niu, Y., Huang, J., Shi, J., Zhang, H.: Unbiased scene graph generation from biased training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3716\u20133725 (2020)","DOI":"10.1109\/CVPR42600.2020.00377"},{"key":"38_CR24","doi-asserted-by":"crossref","unstructured":"Vakaj, E., Tiwari, S., Mihindukulasooriya, N., Ortiz-Rodr\u00edguez, F., Mcgranaghan, R.: Nlp4kgc: natural language processing for knowledge graph construction. In: Companion Proceedings of the ACM Web Conference 2023, pp. 1111\u20131111 (2023)","DOI":"10.1145\/3543873.3589746"},{"key":"38_CR25","unstructured":"Vossen, P., Ilievski, F., Postma, M., Segers, R.: Don\u2019t annotate, but validate: a data-to-text method for capturing event data. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (2018)"},{"key":"38_CR26","doi-asserted-by":"crossref","unstructured":"Wang, W., Feng, F., He, X., Zhang, H., Chua, T.S.: Clicks can be cheating: counterfactual recommendation for mitigating clickbait issue. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1288\u20131297 (2021)","DOI":"10.1145\/3404835.3462962"},{"key":"38_CR27","doi-asserted-by":"crossref","unstructured":"Yang, D., et\u00a0al.: Context de-confounded emotion recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19005\u201319015 (2023)","DOI":"10.1109\/CVPR52729.2023.01822"},{"issue":"11","key":"38_CR28","first-page":"12996","volume":"45","author":"X Yang","year":"2021","unstructured":"Yang, X., Zhang, H., Cai, J.: Deconfounded image captioning: a causal retrospect. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 12996\u201313010 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR29","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhang, H., Qi, G., Cai, J.: Causal attention for vision-language tasks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9847\u20139857 (2021)","DOI":"10.1109\/CVPR46437.2021.00972"},{"key":"38_CR30","doi-asserted-by":"crossref","unstructured":"Yu, X., Yin, W., Roth, D.: Pairwise representation learning for event coreference. In: Nastase, V., Pavlick, E., Pilehvar, M.T., Camacho-Collados, J., Raganato, A. (eds.) Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2022, Seattle, WA, USA, 14-15 July 2022, pp. 69\u201378. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.starsem-1.6"},{"key":"38_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhai, X., Zhao, Z., Zong, Y., Wen, X., Zhao, B.: What if the tv was off? examining counterfactual reasoning abilities of multi-modal language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21853\u201321862 (2024)","DOI":"10.1109\/CVPR52733.2024.02064"},{"key":"38_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., Zhang, Y.: Causal intervention for leveraging popularity bias in recommendation. In: Proceedings of the 44th international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11\u201320 (2021)","DOI":"10.1145\/3404835.3462875"},{"key":"38_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102769","volume":"115","author":"W Zhao","year":"2025","unstructured":"Zhao, W., Zhang, Y., Wu, D., Wu, F., Jain, N.: Hypergraph convolutional networks with multi-ordering relations for cross-document event coreference resolution. Inform. Fusion 115, 102769 (2025)","journal-title":"Inform. Fusion"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4987-0_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:29:38Z","timestamp":1768393778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4987-0_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819549863","9789819549870"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4987-0_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"15 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}