{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T08:58:47Z","timestamp":1781168327772,"version":"3.54.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":91,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100016079","name":"Science and Technology Department of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2022TSYCLJ0037"],"award-info":[{"award-number":["2022TSYCLJ0037"]}],"id":[{"id":"10.13039\/100016079","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262065"],"award-info":[{"award-number":["62262065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Program of Xinjiang","award":["2022B01008"],"award-info":[{"award-number":["2022B01008"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022ZD0115800"],"award-info":[{"award-number":["2022ZD0115800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["62476233"],"award-info":[{"award-number":["62476233"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2024B03028"],"award-info":[{"award-number":["2024B03028"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2024B03041"],"award-info":[{"award-number":["2024B03041"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Research and Development Program of Xinjiang","award":["2023B01005"],"award-info":[{"award-number":["2023B01005"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s44443-026-00531-x","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:15:33Z","timestamp":1773317733000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal Semantics Refinement Model for Event Causality Identification via information bottleneck representation learning"],"prefix":"10.1007","volume":"38","author":[{"given":"Rui","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9351-8738","authenticated-orcid":false,"given":"Wenzhong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yabo","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haipeng","family":"Jing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongzhen","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liejun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"531_CR1","doi-asserted-by":"crossref","unstructured":"Aftan S, Shah H (2023) A survey on bert and its applications. In: 2023 20th Learning and Technology Conference (L&T), IEEE, pp 161\u2013166","DOI":"10.1109\/LT58159.2023.10092289"},{"key":"531_CR2","doi-asserted-by":"crossref","unstructured":"Al-Khatib K, Hou Y, Wachsmuth H, et\u00a0al (2020) End-to-end argumentation knowledge graph construction. In: Proceedings of the AAAI conference on artificial intelligence, pp 7367\u20137374","DOI":"10.1609\/aaai.v34i05.6231"},{"key":"531_CR3","doi-asserted-by":"crossref","unstructured":"Ammanabrolu P, Cheung W, Broniec W, et\u00a0al (2021) Automated storytelling via causal, commonsense plot ordering. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 5859\u20135867","DOI":"10.1609\/aaai.v35i7.16733"},{"key":"531_CR4","unstructured":"Beamer B, Rozovskaya A, Girju R (2008) Automatic semantic relation extraction with multiple boundary generation. In: AAAI, pp 824\u2013829"},{"key":"531_CR5","doi-asserted-by":"crossref","unstructured":"Berant J, Srikumar V, Chen PC, et\u00a0al (2014) Modeling biological processes for reading comprehension. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1499\u20131510","DOI":"10.3115\/v1\/D14-1159"},{"key":"531_CR6","unstructured":"Breja M, Jain SK (2020) Causality for question answering. In: COLINS, pp 884\u2013893"},{"key":"531_CR7","doi-asserted-by":"crossref","unstructured":"Cao P, Zuo X, Chen Y, et\u00a0al (2021) Knowledge-enriched event causality identification via latent structure induction networks. 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 4862\u20134872","DOI":"10.18653\/v1\/2021.acl-long.376"},{"key":"531_CR8","doi-asserted-by":"crossref","unstructured":"Caselli T, Vossen P (2017) The event storyline corpus: A new benchmark for causal and temporal relation extraction. In: Proceedings of the Events and Stories in the News Workshop, pp 77\u201386","DOI":"10.18653\/v1\/W17-2711"},{"key":"531_CR9","doi-asserted-by":"crossref","unstructured":"Chao L, Xiang W, Wang B (2024) In-context contrastive learning for event causality identification. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp 868\u2013881","DOI":"10.18653\/v1\/2024.emnlp-main.51"},{"key":"531_CR10","unstructured":"Chen M, Cao Y, Deng K, et\u00a0al (2022) ERGO: Event relational graph transformer for document-level event causality identification. In: Calzolari N, Huang CR, Kim H, et\u00a0al (eds) Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Gyeongju, Republic of Korea, pp 2118\u20132128. https:\/\/aclanthology.org\/2022.coling-1.185\/"},{"key":"531_CR11","doi-asserted-by":"publisher","unstructured":"Chen J, Feng J, Hu J, et\u00a0al (2019) Causal analysis of learning performance based on bayesian network and mutual information. Entropy 21(11). https:\/\/doi.org\/10.3390\/e21111102https:\/\/www.mdpi.com\/1099-4300\/21\/11\/1102","DOI":"10.3390\/e21111102"},{"key":"531_CR12","doi-asserted-by":"crossref","unstructured":"Cheng F, Miyao Y (2017) Classifying temporal relations by bidirectional lstm over dependency paths. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 1\u20136","DOI":"10.18653\/v1\/P17-2001"},{"key":"531_CR13","doi-asserted-by":"publisher","unstructured":"Choubey PK, Huang R (2017) A sequential model for classifying temporal relations between intra-sentence events. In: Palmer M, Hwa R, Riedel S (eds) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, pp 1796\u20131802. https:\/\/doi.org\/10.18653\/v1\/D17-1190https:\/\/aclanthology.org\/D17-1190\/","DOI":"10.18653\/v1\/D17-1190"},{"key":"531_CR14","doi-asserted-by":"publisher","unstructured":"Conneau A, Khandelwal K, Goyal N, et\u00a0al (2020) Unsupervised cross-lingual representation learning at scale. In: Jurafsky D, Chai J, Schluter N, et\u00a0al (eds) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440\u20138451. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.747https:\/\/aclanthology.org\/2020.acl-main.747\/","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"531_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119905","volume":"656","author":"L Ding","year":"2024","unstructured":"Ding L, Chen J, Du P et al (2024) Event causality identification via graph contrast-based knowledge augmented networks. Inf Sci 656:119905","journal-title":"Inf Sci"},{"key":"531_CR16","doi-asserted-by":"crossref","unstructured":"Gao L, Choubey PK, Huang R (2019) Modeling document-level causal structures for event causal relation identification. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 1808\u20131817","DOI":"10.18653\/v1\/N19-1179"},{"key":"531_CR17","doi-asserted-by":"crossref","unstructured":"Garcia D, EDF-DER, IMA-TIEM (1997) Coatis, an nlp system to locate expressions of actions connected by causality links. In: International Conference on Knowledge Engineering and Knowledge Management, Springer, pp 347\u2013352","DOI":"10.1007\/BFb0026799"},{"key":"531_CR18","doi-asserted-by":"crossref","unstructured":"Girju R (2003) Automatic detection of causal relations for question answering. In: Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering, pp 76\u201383","DOI":"10.3115\/1119312.1119322"},{"issue":"7","key":"531_CR19","first-page":"7569","volume":"35","author":"S Guan","year":"2022","unstructured":"Guan S, Cheng X, Bai L et al (2022) What is event knowledge graph: A survey. IEEE Trans Knowl Data Eng 35(7):7569\u20137589","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"531_CR20","doi-asserted-by":"crossref","unstructured":"Hashimoto C (2019) Weakly supervised multilingual causality extraction from wikipedia. 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 2988\u20132999","DOI":"10.18653\/v1\/D19-1296"},{"key":"531_CR21","doi-asserted-by":"crossref","unstructured":"Hidey C, McKeown K (2016) Identifying causal relations using parallel wikipedia articles. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1424\u20131433","DOI":"10.18653\/v1\/P16-1135"},{"key":"531_CR22","unstructured":"Hosseini P, Broniatowski DA, Diab M (2021) Predicting directionality in causal relations in text. arXiv:2103.13606"},{"key":"531_CR23","doi-asserted-by":"publisher","unstructured":"Hosseini P, Broniatowski DA, Diab M (2022) Knowledge-augmented language models for cause-effect relation classification. In: Bosselut A, Li X, Lin BY, et\u00a0al (eds) Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022). Association for Computational Linguistics, Dublin, Ireland, pp 43\u201348. https:\/\/doi.org\/10.18653\/v1\/2022.csrr-1.6https:\/\/aclanthology.org\/2022.csrr-1.6\/","DOI":"10.18653\/v1\/2022.csrr-1.6"},{"key":"531_CR24","doi-asserted-by":"publisher","unstructured":"Hu Z, Li Z, Jin X, et\u00a0al (2023) Semantic structure enhanced event causality identification. In: Rogers A, Boyd-Graber J, Okazaki N (eds) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Toronto, Canada, pp 10901\u201310913. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.610https:\/\/aclanthology.org\/2023.acl-long.610\/","DOI":"10.18653\/v1\/2023.acl-long.610"},{"key":"531_CR25","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1162\/tacl_a_00300","volume":"8","author":"M Joshi","year":"2020","unstructured":"Joshi M, Chen D, Liu Y et al (2020) Spanbert: Improving pre-training by representing and predicting spans. Trans Assoc Comput Linguis 8:64\u201377","journal-title":"Trans Assoc Comput Linguis"},{"key":"531_CR26","doi-asserted-by":"crossref","unstructured":"Kadowaki K, Iida R, Torisawa K, et\u00a0al (2019) Event causality recognition exploiting multiple annotators\u2019 judgments and background knowledge. 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 5816\u20135822","DOI":"10.18653\/v1\/D19-1590"},{"key":"531_CR27","doi-asserted-by":"crossref","unstructured":"Kruengkrai C, Torisawa K, Hashimoto C, et\u00a0al (2017) Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11005"},{"key":"531_CR28","unstructured":"Lai VD, Veyseh APB, Van\u00a0Nguyen M, et\u00a0al (2022) Meci: A multilingual dataset for event causality identification. In: Proceedings of the 29th international conference on computational linguistics, pp 2346\u20132356"},{"key":"531_CR29","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.eswa.2018.08.009","volume":"115","author":"P Li","year":"2019","unstructured":"Li P, Mao K (2019) Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts. Expert Syst Appl 115:512\u2013523","journal-title":"Expert Syst Appl"},{"key":"531_CR30","doi-asserted-by":"publisher","unstructured":"Li H, Gao Q, Wu H, et\u00a0al (2024a) Advancing event causality identification via heuristic semantic dependency inquiry network. In: Al-Onaizan Y, Bansal M, Chen YN (eds) Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Miami, Florida, USA, pp 1467\u20131478. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.87https:\/\/aclanthology.org\/2024.emnlp-main.87\/","DOI":"10.18653\/v1\/2024.emnlp-main.87"},{"key":"531_CR31","doi-asserted-by":"publisher","unstructured":"Li J, Tang T, Zhao WX, et\u00a0al (2024b) Pre-trained language models for text generation: A survey. ACM Comput Surv 56(9). https:\/\/doi.org\/10.1145\/3649449","DOI":"10.1145\/3649449"},{"issue":"4","key":"531_CR32","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1023\/B:BTTJ.0000047600.45421.6d","volume":"22","author":"H Liu","year":"2004","unstructured":"Liu H, Singh P (2004) Conceptnet\u2014a practical commonsense reasoning tool-kit. BT Technol J 22(4):211\u2013226","journal-title":"BT Technol J"},{"key":"531_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110064","volume":"259","author":"J Liu","year":"2023","unstructured":"Liu J, Zhang Z, Guo Z et al (2023) Kept: Knowledge enhanced prompt tuning for event causality identification. Knowl-Based Syst 259:110064","journal-title":"Knowl-Based Syst"},{"key":"531_CR34","doi-asserted-by":"crossref","unstructured":"Liu J, Chen Y, Zhao J (2021) Knowledge enhanced event causality identification with mention masking generalizations. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 3608\u20133614","DOI":"10.24963\/ijcai.2020\/499"},{"key":"531_CR35","unstructured":"Liu Y, Ott M, Goyal N, et\u00a0al (2019) Roberta: A robustly optimized bert pretraining approach. arXiv:1907.11692"},{"key":"531_CR36","doi-asserted-by":"publisher","unstructured":"Liu C, Xiang W, Wang B (2024) Identifying while learning for document event causality identification. In: Ku LW, Martins A, Srikumar V (eds) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Bangkok, Thailand, pp 3815\u20133827. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.210https:\/\/aclanthology.org\/2024.acl-long.210\/","DOI":"10.18653\/v1\/2024.acl-long.210"},{"key":"531_CR37","doi-asserted-by":"crossref","unstructured":"Man H, Nguyen MV, Nguyen TH (2022) Event causality identification via generation of important context words. In: Proceedings of the 11th Joint Conference on Lexical and Computational Semantics (* SEM) at NAACL 2022","DOI":"10.18653\/v1\/2022.starsem-1.28"},{"key":"531_CR38","doi-asserted-by":"crossref","unstructured":"Mirza P (2014) Extracting temporal and causal relations between events. In: Proceedings of the ACL 2014 Student Research Workshop, pp 10\u201317","DOI":"10.3115\/v1\/P14-3002"},{"key":"531_CR39","unstructured":"Mirza P, Tonelli S (2014) An analysis of causality between events and its relation to temporal information. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp 2097\u20132106"},{"key":"531_CR40","doi-asserted-by":"crossref","unstructured":"Oh JH, Torisawa K, Hashimoto C, et\u00a0al (2016) A semi-supervised learning approach to why-question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v30i1.10388"},{"key":"531_CR41","doi-asserted-by":"crossref","unstructured":"Phu MT, Nguyen TH (2021) Graph convolutional networks for event causality identification with rich document-level structures. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: Human language technologies, pp 3480\u20133490","DOI":"10.18653\/v1\/2021.naacl-main.273"},{"key":"531_CR42","doi-asserted-by":"crossref","unstructured":"Radinsky K, Davidovich S, Markovitch S (2012) Learning causality for news events prediction. In: Proceedings of the 21st international conference on World Wide Web, pp 909\u2013918","DOI":"10.1145\/2187836.2187958"},{"key":"531_CR43","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"531_CR44","unstructured":"Sun L, Nikolaev AG (2016) Mutual information based matching for causal inference with observational data. Journal of Machine Learning Research 17(199):1\u201331. http:\/\/jmlr.org\/papers\/v17\/15-420.html"},{"key":"531_CR45","unstructured":"Su Y, Zhang H, Zhang G, et\u00a0al (2025) Enhancing event causality identification with llm knowledge and concept-level event relations. In: Proceedings of the 31st International Conference on Computational Linguistics, pp 7403\u20137414"},{"key":"531_CR46","doi-asserted-by":"crossref","unstructured":"Tran MP, Nguyen MV, Nguyen TH (2021) Fine-grained temporal relation extraction with ordered-neuron lstm and graph convolutional networks. In: Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021) at EMNLP 2021","DOI":"10.18653\/v1\/2021.wnut-1.5"},{"issue":"110","key":"531_CR47","first-page":"261","volume":"110","author":"C Walker","year":"2006","unstructured":"Walker C, Strassel S, Medero J et al (2006) Ace 2005 multilingual training corpus. Prog Theor Phys Suppl 110(110):261\u2013276","journal-title":"Prog Theor Phys Suppl"},{"key":"531_CR48","doi-asserted-by":"crossref","unstructured":"Wang H, Li J, Wu H et al (2023) Pre-trained language models and their applications. Engineering 25:51\u201365","DOI":"10.1016\/j.eng.2022.04.024"},{"key":"531_CR49","doi-asserted-by":"crossref","unstructured":"Wu S, Zhao R, Zheng Y, et\u00a0al (2023) Identify event causality with knowledge and analogy. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 13745\u201313753","DOI":"10.1609\/aaai.v37i11.26610"},{"issue":"8","key":"531_CR50","doi-asserted-by":"publisher","first-page":"5692","DOI":"10.1109\/TPAMI.2024.3371473","volume":"46","author":"Z Yang","year":"2024","unstructured":"Yang Z, Ding M, Huang T et al (2024) Does negative sampling matter? a review with insights into its theory and applications. IEEE Trans Pattern Anal Mach Intell 46(8):5692\u20135711. https:\/\/doi.org\/10.1109\/TPAMI.2024.3371473","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"531_CR51","unstructured":"Yun S, Jeong M, Kim R, et\u00a0al (2019) Graph transformer networks. Adv Neural Inf Process Syst 32"},{"key":"531_CR52","doi-asserted-by":"crossref","unstructured":"Zhang P, Zhou L, Li Y et al (2025) Self-supervised learning of invariant causal representation in heterogeneous information network. Inf Fusion 123:10324","DOI":"10.1016\/j.inffus.2025.103246"},{"key":"531_CR53","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ins.2021.01.078","volume":"561","author":"K Zhao","year":"2021","unstructured":"Zhao K, Ji D, He F et al (2021) Document-level event causality identification via graph inference mechanism. Inf Sci 561:115\u2013129","journal-title":"Inf Sci"},{"issue":"3","key":"531_CR54","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1007\/s13042-024-02367-1","volume":"16","author":"E Zhu","year":"2025","unstructured":"Zhu E, Yu Z, Huang Y et al (2025) Ptekc: pre-training with event knowledge of conceptnet for cross-lingual event causality identification. Int J Mach Learn Cybern 16(3):1859\u20131872","journal-title":"Int J Mach Learn Cybern"},{"key":"531_CR55","doi-asserted-by":"publisher","unstructured":"Zuo X, Cao P, Chen Y, et\u00a0al (2021a) Improving event causality identification via self-supervised representation learning on external causal statement. In: Zong C, Xia F, Li W, et\u00a0al (eds) Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online, pp 2162\u20132172. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.190https:\/\/aclanthology.org\/2021.findings-acl.190\/","DOI":"10.18653\/v1\/2021.findings-acl.190"},{"key":"531_CR56","doi-asserted-by":"publisher","unstructured":"Zuo X, Cao P, Chen Y, et\u00a0al (2021b) LearnDA: Learnable knowledge-guided data augmentation for event causality identification. In: Zong C, Xia F, Li W, et\u00a0al (eds) 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). Association for Computational Linguistics, Online, pp 3558\u20133571. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.276https:\/\/aclanthology.org\/2021.acl-long.276\/","DOI":"10.18653\/v1\/2021.acl-long.276"},{"key":"531_CR57","doi-asserted-by":"publisher","unstructured":"Zuo X, Chen Y, Liu K, et\u00a0al (2020) KnowDis: Knowledge enhanced data augmentation for event causality detection via distant supervision. In: Scott D, Bel N, Zong C (eds) Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Barcelona, Spain (Online), pp 1544\u20131550. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.135https:\/\/aclanthology.org\/2020.coling-main.135\/","DOI":"10.18653\/v1\/2020.coling-main.135"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-026-00531-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00531-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00531-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T08:13:25Z","timestamp":1781165605000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-026-00531-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["531"],"URL":"https:\/\/doi.org\/10.1007\/s44443-026-00531-x","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,12]]},"assertion":[{"value":"23 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"215"}}