{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T04:01:05Z","timestamp":1749355265069,"version":"3.41.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Guangzhou Basic and Applied Basic Research Foundation","award":["2023A04J1687","2023A04J1687","2023A04J1687","2023A04J1687"],"award-info":[{"award-number":["2023A04J1687","2023A04J1687","2023A04J1687","2023A04J1687"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02446-3","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T07:27:18Z","timestamp":1731310038000},"page":"3233-3251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Zero-shot event argument extraction by disentangling trigger from argument and role"],"prefix":"10.1007","volume":"16","author":[{"given":"Zhengdong","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqian","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanlin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weikai","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiping","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"2446_CR1","doi-asserted-by":"crossref","unstructured":"Huang L, Ji H, Cho K, Dagan I, Riedel S, Voss C (2018) Zero-shot transfer learning for event extraction. In:Proceedings of Association for Computational Linguistics page:2160\u20132170","DOI":"10.18653\/v1\/P18-1201"},{"key":"2446_CR2","doi-asserted-by":"crossref","unstructured":"Du X, Cardie C (2020) Event extraction by answering (almost) natural questions. In: Proceedings of Empirical Methods in Natural Language Processing page, 671\u2013683","DOI":"10.18653\/v1\/2020.emnlp-main.49"},{"key":"2446_CR3","doi-asserted-by":"crossref","unstructured":"Li S, Ji H, Han J (2021) Document-level event argument extraction by conditional generation. In: Proceedings of The North American Chapter of the Association for Computational Linguistics Human: Language Technologies page:894\u2013908","DOI":"10.18653\/v1\/2021.naacl-main.69"},{"key":"2446_CR4","doi-asserted-by":"crossref","unstructured":"Wang S, Yu M, Chang S, Sun L, Huang L (2022) Query and extract: Refining event extraction as type-oriented binary decoding. In: Findings of Association for Computational Linguistics page.169\u2013182","DOI":"10.18653\/v1\/2022.findings-acl.16"},{"key":"2446_CR5","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang X, Han X, Lin Y, Hou L, Liu Z, Li P, Li J, Zhou J (2021) Cleve: contrastive pre-training for event extraction. In: Proceedings of Association for Computational Linguistics page. 6283\u20136297","DOI":"10.18653\/v1\/2021.acl-long.491"},{"key":"2446_CR6","doi-asserted-by":"crossref","unstructured":"Lyu Q, Zhang H, Sulem E, Roth D (2021) Zero-shot event extraction via transfer learning: challenges and insights. In: Proceedings of Association for Computational Linguistics page. 322\u2013332","DOI":"10.18653\/v1\/2021.acl-short.42"},{"key":"2446_CR7","doi-asserted-by":"crossref","unstructured":"Zhang H, Yao W, Yu D (2022) Efficient zero-shot event extraction with context-definition alignment. In: Findings of Empirical Methods in Natural Language Processing","DOI":"10.18653\/v1\/2022.findings-emnlp.531"},{"key":"2446_CR8","doi-asserted-by":"crossref","unstructured":"Zhang S, Ji T, Ji W, Wang X (2022) Zero-shot event detection based on ordered contrastive learning and prompt-based prediction. In: Findings of North American Chapter of the Association for Computational Linguistics, pp. 2572\u20132580","DOI":"10.18653\/v1\/2022.findings-naacl.196"},{"key":"2446_CR9","unstructured":"Banarescu L, Bonial C, Cai S, Georgescu M, Griffitt K, Hermjakob U, Knight K, Koehn P, Palmer M, Schneider N (2013) Abstract meaning representation for sembanking. In: Proceedings of Linguistic Annotation Workshop and Interoperability with Discourse, pp. 178\u2013186"},{"issue":"12","key":"2446_CR10","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2446_CR11","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Proceedings of Conference on Neural Information Processing Systems, pp. 2787\u20132795"},{"key":"2446_CR12","unstructured":"Choudhary S, Luthra T, Mittal A, Singh R (2021) A survey of knowledge graph embedding and their applications. arXiv preprint arXiv:2107.07842"},{"key":"2446_CR13","doi-asserted-by":"crossref","unstructured":"Peng H, Zhang J, Huang X, Hao Z, Li A, Yu Z, Yu PS (2024) Unsupervised social bot detection via structural information theory. ACM Trans Inf Syst","DOI":"10.1145\/3660522"},{"issue":"1","key":"2446_CR14","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TPAMI.2022.3144993","volume":"45","author":"H Peng","year":"2023","unstructured":"Peng H, Zhang R, Li S, Cao Y, Pan S, Yu PS (2023) Reinforced, incremental and cross-lingual event detection from social messages. IEEE Trans Pattern Anal Mach Intell 45(1):980\u2013998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2446_CR15","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1109\/TASLP.2024.3402087","volume":"32","author":"X Huang","year":"2024","unstructured":"Huang X, Peng H, Zou D, Liu Z, Li J, Liu K, Wu J, Su J, Yu PS (2024) Cosent: Consistent sentence embedding via similarity ranking. IEEE\/ACM Trans Audio Speech Lang Process 32:2800\u20132813","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"2446_CR16","unstructured":"Lafferty J, McCallum A, Pereira FC (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data, 282\u2013289"},{"key":"2446_CR17","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of The North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171\u20134186"},{"key":"2446_CR18","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"2446_CR19","doi-asserted-by":"crossref","unstructured":"Cao Y, Peng H, Yu Z, Philip SY (2024) Hierarchical and incremental structural entropy minimization for unsupervised social event detection. In: Proceedings of the Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, vol. 38, pp. 8255\u20138264","DOI":"10.1609\/aaai.v38i8.28666"},{"key":"2446_CR20","first-page":"24","volume":"20","author":"P Li","year":"2024","unstructured":"Li P, Yu X, Peng H, Xian Y, Wang L, Sun L, Zhang J, Yu PS (2024) Relational prompt-based pre-trained language models for social event detection. ACM Trans Inf Syst 20:24","journal-title":"ACM Trans Inf Syst"},{"key":"2446_CR21","first-page":"1","volume":"2","author":"L Zhao","year":"2024","unstructured":"Zhao L, Zeng Z (2024) Dap-simt: divergence-based adaptive policy for simultaneous machine translation. Int J Mach Learn Cybern 2:1\u201320","journal-title":"Int J Mach Learn Cybern"},{"key":"2446_CR22","unstructured":"Masolo C, Vieu L, Bottazzi E, Catenacci C, Ferrario R, Gangemi A, Guarino N (2004) Social roles and their descriptions. In: International Conference on Principles of Knowledge Representation and Reasoning, pp. 267\u2013277"},{"key":"2446_CR23","unstructured":"Mizoguchi R, Kozaki K, Kitamura Y (2012) Ontological analyses of roles. In: Federated Conference on Computer Science and Information Systems, pp. 489\u2013496"},{"key":"2446_CR24","doi-asserted-by":"crossref","unstructured":"Hsu I-H, Huang K-H, Boschee E, Miller S, Natarajan P, Chang K-W, Peng N (2022) DEGREE: A data-efficient generation-based event extraction model. In: Proceedings of North American Chapter of the Association for Computational Linguistics, pp. 1890\u20131908","DOI":"10.18653\/v1\/2022.naacl-main.138"},{"key":"2446_CR25","doi-asserted-by":"crossref","unstructured":"Sainz O, Gonzalez-Dios I, Lacalle OL, Min B, Agirre E (2022) Textual entailment for event argument extraction: Zero- and few-shot with multi-source learning. In: Findings of North American Chapter of the Association for Computational Linguistics, pp. 2439\u20132455","DOI":"10.18653\/v1\/2022.findings-naacl.187"},{"key":"2446_CR26","unstructured":"Li B, Fang G, Yang Y, Wang Q, Ye W, Zhao W, Zhang S (2023) Evaluating chatgpt\u2019s information extraction capabilities: An assessment of performance, explainability, calibration, and faithfulness. arXiv:abs\/2304.11633"},{"key":"2446_CR27","doi-asserted-by":"crossref","unstructured":"Zhou H, Qian J, Feng Z, Hui L, Zhu Z, Mao K (2024) LLMs learn task heuristics from demonstrations: A heuristic-driven prompting strategy for document-level event argument extraction. In: Ku L-W, Martins A, Srikumar V (eds) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 11972\u201311990","DOI":"10.18653\/v1\/2024.acl-long.647"},{"key":"2446_CR28","unstructured":"OpenAI: GPT-4 Technical Report (2023). arXiv:abs\/2303.08774"},{"key":"2446_CR29","doi-asserted-by":"crossref","unstructured":"Lin Y, Ji H, Huang F, Wu L (2020) A joint neural model for information extraction with global features. In: Proceedings of Association for Computational Linguistics, pp. 7999\u20138009","DOI":"10.18653\/v1\/2020.acl-main.713"},{"key":"2446_CR30","doi-asserted-by":"crossref","unstructured":"Wadden D, Wennberg U, Luan Y, Hajishirzi H (2019) Entity, relation, and event extraction with contextualized span representations. In: Proceedings of Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing, pp. 5784\u20135789","DOI":"10.18653\/v1\/D19-1585"},{"key":"2446_CR31","first-page":"1","volume":"13","author":"X Ge","year":"2014","unstructured":"Ge X, Wang YC, Wang B, Kuo C-CJ et al (2014) Knowledge graph embedding: an overview. APSIPA Trans Signal Inf Process 13:1","journal-title":"APSIPA Trans Signal Inf Process"},{"key":"2446_CR32","unstructured":"Sun Z, Deng Z-H, Nie J-Y, Tang J (2019) Rotate: knowledge graph embedding by relational rotation in complex space. In: Proceedings of International Conference on Learning Representations"},{"key":"2446_CR33","unstructured":"Yang B, Yih Wt, He X, Gao J, Deng L (2014) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of International Conference on Learning Representations"},{"key":"2446_CR34","unstructured":"Trouillon T, Welbl J, Riedel S, Gaussier E, Bouchard G (2016) Complex embeddings for simple link prediction. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 2071\u20132080. PMLR, New York"},{"issue":"140","key":"2446_CR35","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(140):1\u201367","journal-title":"J Mach Learn Res"},{"key":"2446_CR36","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang H, Roth D (2021) Zero-shot label-aware event trigger and argument classification. In: Findings of Association for Computational Linguistics, pp. 1331\u20131340","DOI":"10.18653\/v1\/2021.findings-acl.114"},{"key":"2446_CR37","unstructured":"Shi P, Lin J (2019) Simple bert models for relation extraction and semantic role labeling. arXiv preprint arXiv:1904.05255"},{"key":"2446_CR38","doi-asserted-by":"crossref","unstructured":"Li S, Ji H, Han J (2021) Document-level event argument extraction by conditional generation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 894\u2013908","DOI":"10.18653\/v1\/2021.naacl-main.69"},{"key":"2446_CR39","doi-asserted-by":"crossref","unstructured":"Hsu I-H, Xie Z, Huang K-H, Natarajan P, Peng N (2023) AMPERE: AMR-aware prefix for generation-based event argument extraction model. 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), pp. 10976\u201310993. Association for Computational Linguistics, Toronto, Canada","DOI":"10.18653\/v1\/2023.acl-long.615"},{"key":"2446_CR40","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2020) BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Jurafsky D, Chai J, Schluter N, Tetreault J (eds) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880. Association for Computational Linguistics, Online (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02446-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02446-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02446-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:29:35Z","timestamp":1749270575000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02446-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,11]]},"references-count":40,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2446"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02446-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,11,11]]},"assertion":[{"value":"13 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}