{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:07:58Z","timestamp":1774915678625,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981809","type":"print"},{"value":"9789819981816","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8181-6_33","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:30Z","timestamp":1701039750000},"page":"434-446","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Three-Stage Framework for\u00a0Event-Event Relation Extraction with\u00a0Large Language Model"],"prefix":"10.1007","author":[{"given":"Feng","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YueTong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZhiXiao","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"BingKun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YongFeng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SongBin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"33_CR1","unstructured":"Bubeck, S., et al.: Sparks of artificial general intelligence: Early experiments with GPT-4. CoRR abs\/2303.12712 (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.12712"},{"key":"33_CR2","doi-asserted-by":"publisher","unstructured":"Caselli, T., Vossen, P.: The event storyline corpus: A new benchmark for causal and temporal relation extraction. In: Caselli, T., et al. (eds.) Proceedings of the Events and Stories in the News Workshop@ACL 2017, Vancouver, Canada, August 4, 2017, pp. 77\u201386. Association for Computational Linguistics (2017). https:\/\/doi.org\/10.18653\/v1\/w17-2711. https:\/\/doi.org\/10.18653\/v1\/w17-2711","DOI":"10.18653\/v1\/w17-2711"},{"key":"33_CR3","unstructured":"Jiao, W., Wang, W., Huang, J., Wang, X., Tu, Z.: Is chatgpt A good translator? A preliminary study. CoRR abs\/2301.08745 (2023). https:\/\/doi.org\/10.48550\/arXiv.2301.08745"},{"key":"33_CR4","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. In: NeurIPS (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/8bb0d291acd4acf06ef112099c16f326-Abstract-Conference.html"},{"key":"33_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1007\/978-3-030-60457-8_44","volume-title":"Natural Language Processing and Chinese Computing","author":"X Li","year":"2020","unstructured":"Li, X., et al.: DuEE: a large-scale dataset for Chinese event extraction in real-world scenarios. In: Zhu, X., Zhang, M., Hong, Yu., He, R. (eds.) NLPCC 2020. LNCS (LNAI), vol. 12431, pp. 534\u2013545. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60457-8_44"},{"key":"33_CR6","unstructured":"Luo, Z., Xie, Q., Ananiadou, S.: Chatgpt as a factual inconsistency evaluator for abstractive text summarization. CoRR abs\/2303.15621 (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.15621"},{"key":"33_CR7","doi-asserted-by":"publisher","unstructured":"Man, H., Nguyen, M., Nguyen, T.: Event causality identification via generation of important context words. 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, July 14\u201315, 2022, pp. 323\u2013330. Association for Computational, Linguistics (2022). https:\/\/doi.org\/10.18653\/v1\/2022.starsem-1.28","DOI":"10.18653\/v1\/2022.starsem-1.28"},{"key":"33_CR8","doi-asserted-by":"publisher","unstructured":"Mirza, P.: Extracting temporal and causal relations between events. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, June 22\u201327, 2014, Baltimore, MD, USA, Student Research Workshop, pp. 10\u201317. The Association for Computer Linguistics (2014). https:\/\/doi.org\/10.3115\/v1\/p14-3002","DOI":"10.3115\/v1\/p14-3002"},{"key":"33_CR9","doi-asserted-by":"publisher","unstructured":"Naik, A., Breitfeller, L., Ros\u00e9, C.P.: Tddiscourse: A dataset for discourse-level temporal ordering of events. In: Nakamura, S., Gasic, M., Zuckerman, I., Skantze, G., Nakano, M., Papangelis, A., Ultes, S., Yoshino, K. (eds.) Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, SIGdial 2019, Stockholm, Sweden, September 11\u201313 2019, pp. 239\u2013249. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/W19-5929","DOI":"10.18653\/v1\/W19-5929"},{"key":"33_CR10","doi-asserted-by":"publisher","unstructured":"Ning, Q., Feng, Z., Wu, H., Roth, D.: Joint reasoning for temporal and causal relations. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15\u201320, 2018, Volume 1: Long Papers, pp. 2278\u20132288. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1212","DOI":"10.18653\/v1\/P18-1212"},{"key":"33_CR11","doi-asserted-by":"publisher","unstructured":"Ning, Q., Subramanian, S., Roth, D.: An improved neural baseline for temporal relation extraction. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3\u20137, 2019, pp. 6202\u20136208. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1642","DOI":"10.18653\/v1\/D19-1642"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Ning, Q., Wu, H., Roth, D.: A multi-axis annotation scheme for event temporal relations. ArXiv abs\/1804.07828 (2018)","DOI":"10.18653\/v1\/P18-1122"},{"key":"33_CR13","doi-asserted-by":"publisher","unstructured":"Ning, Q., Zhou, B., Feng, Z., Peng, H., Roth, D.: Cogcomptime: a tool for understanding time in natural language. In: Blanco, E., Lu, W. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018: System Demonstrations, Brussels, Belgium, October 31 - November 4, 2018, pp. 72\u201377. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/d18-2013","DOI":"10.18653\/v1\/d18-2013"},{"key":"33_CR14","doi-asserted-by":"publisher","unstructured":"Phu, M.T., Nguyen, M.V., Nguyen, T.H.: Fine-grained temporal relation extraction with ordered-neuron LSTM and graph convolutional networks. In: Xu, W., Ritter, A., Baldwin, T., Rahimi, A. (eds.) Proceedings of the Seventh Workshop on Noisy User-generated Text, W-NUT 2021, Online, November 11, 2021, pp. 35\u201345. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.wnut-1.5","DOI":"10.18653\/v1\/2021.wnut-1.5"},{"key":"33_CR15","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140:1\u2013140:67 (2020). http:\/\/jmlr.org\/papers\/v21\/20-074.html"},{"key":"33_CR16","doi-asserted-by":"publisher","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3\u20137, 2019, pp. 3980\u20133990. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"issue":"8","key":"33_CR17","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1109\/TKDE.2017.2690912","volume":"29","author":"S Song","year":"2017","unstructured":"Song, S., Gao, Y., Wang, C., Zhu, X., Wang, J., Yu, P.S.: Matching heterogeneous events with patterns. IEEE Trans. Knowl. Data Eng. 29(8), 1695\u20131708 (2017). https:\/\/doi.org\/10.1109\/TKDE.2017.2690912","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"33_CR18","doi-asserted-by":"publisher","unstructured":"Tan, X., Pergola, G., He, Y.: Extracting event temporal relations via hyperbolic geometry. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event\/Punta Cana, Dominican Republic, 7\u201311 November, 2021, pp. 8065\u20138077. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.636","DOI":"10.18653\/v1\/2021.emnlp-main.636"},{"key":"33_CR19","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: NeurIPS (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html"},{"key":"33_CR20","doi-asserted-by":"publisher","unstructured":"Wei, X., et al.: Zero-shot information extraction via chatting with chatgpt. CoRR abs\/2302.10205 (2023). https:\/\/doi.org\/10.48550\/arXiv.2302.10205","DOI":"10.48550\/arXiv.2302.10205"},{"key":"33_CR21","doi-asserted-by":"publisher","unstructured":"Xian, Y., Schiele, B., Akata, Z.: Zero-shot learning - the good, the bad and the ugly. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326, 2017, pp. 3077\u20133086. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.328","DOI":"10.1109\/CVPR.2017.328"},{"key":"33_CR22","doi-asserted-by":"publisher","unstructured":"Yang, K., Ji, S., Zhang, T., Xie, Q., Ananiadou, S.: On the evaluations of chatgpt and emotion-enhanced prompting for mental health analysis. CoRR abs\/2304.03347 (2023). https:\/\/doi.org\/10.48550\/arXiv.2304.03347","DOI":"10.48550\/arXiv.2304.03347"},{"key":"33_CR23","doi-asserted-by":"publisher","unstructured":"Yang, S., Feng, D., Qiao, L., Kan, Z., Li, D.: Exploring pre-trained language models for event extraction and generation. In: Korhonen, A., Traum, D.R., M\u00e0rquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers. pp. 5284\u20135294. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1522, https:\/\/doi.org\/10.18653\/v1\/p19-1522","DOI":"10.18653\/v1\/p19-1522"},{"key":"33_CR24","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Zhang, A., Li, M., Smola, A.: Automatic chain of thought prompting in large language models. CoRR abs\/2210.03493 (2022). https:\/\/doi.org\/10.48550\/arXiv.2210.03493","DOI":"10.48550\/arXiv.2210.03493"},{"key":"33_CR25","doi-asserted-by":"publisher","unstructured":"Zuo, X., Cao, P., Chen, Y., Liu, K., Zhao, J., Peng, W., Chen, Y.: Learnda: Learnable knowledge-guided data augmentation for event causality identification. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1\u20136, 2021, pp. 3558\u20133571. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.276","DOI":"10.18653\/v1\/2021.acl-long.276"},{"key":"33_CR26","doi-asserted-by":"publisher","unstructured":"Zuo, X., Chen, Y., Liu, K., Zhao, J.: 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, COLING 2020, Barcelona, Spain (Online), December 8\u201313, 2020, pp. 1544\u20131550. International Committee on Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.135","DOI":"10.18653\/v1\/2020.coling-main.135"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8181-6_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:31:15Z","timestamp":1710329475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8181-6_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981809","9789819981816"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8181-6_33","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}