{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:37:24Z","timestamp":1772908644189,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172167"],"award-info":[{"award-number":["62172167"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00521-025-11486-x","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T06:46:55Z","timestamp":1755154015000},"page":"21743-21759","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DAPrompt: deterministic assumption prompt learning for event causality identification"],"prefix":"10.1007","volume":"37","author":[{"given":"Wei","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanhong","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0312-4805","authenticated-orcid":false,"given":"Bang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"11486_CR1","doi-asserted-by":"crossref","unstructured":"Baker CF, Fillmore CJ, Lowe JB (1998) The berkeley framenet project. In: 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics. Quebec, Canada, pp. 86\u201390","DOI":"10.3115\/980845.980860"},{"key":"11486_CR2","unstructured":"Bondarenko A et\u00a0al (2022) Causalqa: A benchmark for causal question answering. In: Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Korea, pp. 3296\u20133308"},{"key":"11486_CR3","doi-asserted-by":"crossref","unstructured":"Cao P 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, Virtual, pp. 4862\u20134872","DOI":"10.18653\/v1\/2021.acl-long.376"},{"key":"11486_CR4","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@ACL 2017, Vancouver, Canada, pp. 77\u201386","DOI":"10.18653\/v1\/W17-2711"},{"key":"11486_CR5","unstructured":"Chen M et\u00a0al (2022) ERGO: event relational graph transformer for document-level event causality identification. In: Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Korea, pp. 2118\u20132128"},{"key":"11486_CR6","first-page":"10804","volume-title":"ACL","author":"M Chen","year":"2023","unstructured":"Chen M et al (2023) CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification. ACL. Canada, Toronto, pp. 10804\u201310816"},{"key":"11486_CR7","first-page":"4171","volume-title":"NAACL","author":"J Devlin","year":"2019","unstructured":"Devlin J et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. NAACL. Minneapolis, MN, USA, pp. 4171\u20134186"},{"key":"11486_CR8","doi-asserted-by":"crossref","unstructured":"Fan C et\u00a0al (2022) Towards event-level causal relation identification. In: ACM SIGIR Conference, Madrid, Spain, pp. 1828\u20131833","DOI":"10.1145\/3477495.3531758"},{"key":"11486_CR9","first-page":"1808","volume-title":"NAACL","author":"L Gao","year":"2019","unstructured":"Gao L et al (2019) Modeling document-level causal structures for event causal relation identification. NAACL. Minneapolis, MN, USA, pp. 1808\u20131817"},{"key":"11486_CR10","unstructured":"He P et al (2021) Deberta: decoding-enhanced bert with disentangled attention. In: 9th International Conference on Learning Representations. Virtual, Austria, pp. 1\u201323"},{"key":"11486_CR11","doi-asserted-by":"crossref","unstructured":"Liu J, Chen Y, Zhao J (2020) Knowledge enhanced event causality identification with mention masking generalizations. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Virtual, Japan, pp. 3608\u20133614","DOI":"10.24963\/ijcai.2020\/499"},{"key":"11486_CR12","unstructured":"Liu P, Yuan W, Fu J et\u00a0al (2021) Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586 (1):1\u201346"},{"key":"11486_CR13","unstructured":"Liu Y et al (2019) Roberta: A robustly optimized BERT pretraining approach. arXiv:1907.11692 pp. 1\u201313"},{"key":"11486_CR14","unstructured":"Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. In: 7th International Conference on Learning Representations. LA, USA, New Orleans, pp. 1\u201318"},{"key":"11486_CR15","doi-asserted-by":"crossref","unstructured":"Miller GA (1995) Wordnet: A lexical database for english. Communications of the ACM pp. 39\u201341","DOI":"10.1145\/219717.219748"},{"key":"11486_CR16","unstructured":"Mirza P, Tonelli S (2014) An analysis of causality between events and its relation to temporal information. In: Proceedings of the 25th International Conference on Computational Linguistics, Dublin, Ireland, pp. 2097\u20132106"},{"key":"11486_CR17","doi-asserted-by":"crossref","unstructured":"Mu F, Li W (2023) Enhancing event causality identification with counterfactual reasoning. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, pp. 967\u2013975","DOI":"10.18653\/v1\/2023.acl-short.83"},{"key":"11486_CR18","doi-asserted-by":"crossref","unstructured":"Phu MT, Nguyen TH (2021) Graph convolutional networks for event causality identification with rich document-level structures. In: NAACL, Online, pp. 3480\u20133490","DOI":"10.18653\/v1\/2021.naacl-main.273"},{"key":"11486_CR19","doi-asserted-by":"crossref","unstructured":"Piao Y, Lee S, Lee D, et\u00a0al (2022) Sparse structure learning via graph neural networks for inductive document classification. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, Virtual, pp. 11165\u201311173","DOI":"10.1609\/aaai.v36i10.21366"},{"key":"11486_CR20","doi-asserted-by":"publisher","first-page":"10314","DOI":"10.18653\/v1\/2023.findings-acl.655","volume-title":"Findings of the Association for Computational Linguistics: ACL 2023","author":"R Pu","year":"2023","unstructured":"Pu R et al (2023) Enhancing event causality identification with event causal label and event pair interaction graph. Findings of the Association for Computational Linguistics: ACL 2023. Canada, Toronto, pp. 10314\u201310322"},{"key":"11486_CR21","doi-asserted-by":"crossref","unstructured":"Scarselli F, Gori M, Tsoi AC et\u00a0al (2009) The graph neural network model. IEEE Transactions on Neural Networks pp. 61\u201380","DOI":"10.1109\/TNN.2008.2005605"},{"key":"11486_CR22","unstructured":"Schuler KK (2006) Verbnet: A broad-coverage, comprehensive verb lexicon. PhD thesis, University of Pennsylvania"},{"key":"11486_CR23","doi-asserted-by":"crossref","unstructured":"Seoh R et al (2021) Open aspect target sentiment classification with natural language prompts. In: Proceedings of the Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic, pp. 6311\u20136322","DOI":"10.18653\/v1\/2021.emnlp-main.509"},{"key":"11486_CR24","unstructured":"Shen S, Zhou H, Wu T et al (2022) Event causality identification via derivative prompt joint learning. In: Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Korea, pp. 2288\u20132299"},{"key":"11486_CR25","doi-asserted-by":"crossref","unstructured":"Speer R, Chin J, Havasi C (2017) Conceptnet 5.5: An open multilingual graph of general knowledge. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, pp. 4444\u20134451","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"11486_CR26","doi-asserted-by":"crossref","unstructured":"Sui Y et al (2022) Causality-aware enhanced model for multi-hop question answering over knowledge graphs. Knowledge Based Systems pp. 108943\u2013108943","DOI":"10.1016\/j.knosys.2022.108943"},{"key":"11486_CR27","unstructured":"Sun Y et al (2019) ERNIE: enhanced representation through knowledge integration. arXiv:1904.09223 pp. 1\u20138"},{"key":"11486_CR28","doi-asserted-by":"publisher","first-page":"4631","DOI":"10.18653\/v1\/2023.findings-acl.283","volume-title":"Findings of the Association for Computational Linguistics: ACL 2023","author":"Z Tao","year":"2023","unstructured":"Tao Z et al (2023) Seag: Structure-aware event causality generation. Findings of the Association for Computational Linguistics: ACL 2023. Canada, Toronto, pp. 4631\u20134644"},{"key":"11486_CR29","doi-asserted-by":"crossref","unstructured":"Wang C et al (2021) Transprompt: Towards an automatic transferable prompting framework for few-shot text classification. In: Proceedings of the Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic, pp. 2792\u20132802","DOI":"10.18653\/v1\/2021.emnlp-main.221"},{"key":"11486_CR30","doi-asserted-by":"crossref","unstructured":"Wang X, Chen Y, Ding N et al (2022) Maven-ere: A unified large-scale dataset for event coreference, temporal, causal, and subevent relation extraction. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, pp. 926\u2013941","DOI":"10.18653\/v1\/2022.emnlp-main.60"},{"issue":"1","key":"11486_CR31","doi-asserted-by":"publisher","first-page":"173111","DOI":"10.1109\/ACCESS.2019.2956831","volume":"7","author":"W Xiang","year":"2019","unstructured":"Xiang W, Wang B (2019) A survey of event extraction from text. IEEE Access 7(1):173111\u2013173137","journal-title":"IEEE Access"},{"key":"11486_CR32","first-page":"902","volume-title":"COLING","author":"W Xiang","year":"2022","unstructured":"Xiang W, Wang Z, Dai L et al (2022) ConnPrompt: Connective-cloze prompt learning for implicit discourse relation recognition. COLING. Gyeongju, Korea, pp. 902\u2013911"},{"key":"11486_CR33","doi-asserted-by":"crossref","unstructured":"Yuan C et al (2023) Discriminative reasoning with sparse event representation for document-level event-event relation extraction. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, pp. 16222\u201316234","DOI":"10.18653\/v1\/2023.acl-long.897"},{"key":"11486_CR34","doi-asserted-by":"crossref","unstructured":"Zhao K, Ji D, He F et al (2021) Document-level event causality identification via graph inference mechanism. Information Science pp. 115\u2013129","DOI":"10.1016\/j.ins.2021.01.078"},{"key":"11486_CR35","doi-asserted-by":"crossref","unstructured":"Zuo X et al (2021a) Improving event causality identification via self-supervised representation learning on external causal statement. In: Findings of the Association for Computational Linguistics, Virtual, pp. 2162\u20132172","DOI":"10.18653\/v1\/2021.findings-acl.190"},{"key":"11486_CR36","doi-asserted-by":"crossref","unstructured":"Zuo X et al (2021b) LearnDA: Learnable knowledge-guided data augmentation for event causality identification. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, Virtual, pp. 3558\u20133571","DOI":"10.18653\/v1\/2021.acl-long.276"},{"key":"11486_CR37","doi-asserted-by":"crossref","unstructured":"Zuo X, Chen Y, Liu K et al (2020) KnowDis: Knowledge enhanced data augmentation for event causality detection via distant supervision. In: Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, pp. 1544\u20131550","DOI":"10.18653\/v1\/2020.coling-main.135"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11486-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11486-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11486-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:18:52Z","timestamp":1757456332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11486-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,14]]},"references-count":37,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["11486"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11486-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,14]]},"assertion":[{"value":"28 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no Conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}