{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:25:45Z","timestamp":1743017145430,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031446955"},{"type":"electronic","value":"9783031446962"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44696-2_23","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T09:03:59Z","timestamp":1696669439000},"page":"287-298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Well-Separated and\u00a0Representative Prototypes for\u00a0Few-Shot Event Detection"],"prefix":"10.1007","author":[{"given":"Xintong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Shasha","family":"Li","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Grishman, R.: Event detection and domain adaptation with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 365\u2013371 (2015)","DOI":"10.3115\/v1\/P15-2060"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 167\u2013176 (2015)","DOI":"10.3115\/v1\/P15-1017"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Grishman, R.: Modeling skip-grams for event detection with convolutional neural networks. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 886\u2013891 (2016)","DOI":"10.18653\/v1\/D16-1085"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Cho, K., Grishman, R.: Joint event extraction via recurrent neural networks. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 300\u2013309 (2016)","DOI":"10.18653\/v1\/N16-1034"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Fu, L., Cho, K., Grishman, R.: A two-stage approach for extending event detection to new types via neural networks. In: Proceedings of the 1st Workshop on Representation Learning for NLP, pp. 158\u2013165 (2016)","DOI":"10.18653\/v1\/W16-1618"},{"key":"23_CR6","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Huang, L., Ji, H., Cho, K., Voss, C.R.: Zero-shot transfer learning for event extraction. arXiv preprint arXiv:1707.01066 (2017)","DOI":"10.18653\/v1\/P18-1201"},{"key":"23_CR8","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"23_CR10","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"9","key":"23_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-017-9359-x","volume":"61","author":"X Feng","year":"2018","unstructured":"Feng, X., Qin, B., Liu, T.: A language-independent neural network for event detection. Sci. China Inf. Sci. 61(9), 1\u201312 (2018). https:\/\/doi.org\/10.1007\/s11432-017-9359-x","journal-title":"Sci. China Inf. Sci."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Nguyen, T., Grishman, R.: Graph convolutional networks with argument-aware pooling for event detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.12039"},{"key":"23_CR13","unstructured":"Liu, S., Li, Y., Zhang, F., Yang, T., Zhou, X.: Event detection without triggers. 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. 735\u2013744 (2019)"},{"key":"23_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/978-3-030-47436-2_18","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"VD Lai","year":"2020","unstructured":"Lai, V.D., Dernoncourt, F., Nguyen, T.H.: Exploiting the matching information in the support set for few shot event classification. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 233\u2013245. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47436-2_18"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Lai, V.D., Dernoncourt, F., Nguyen, T.H.: Extensively matching for few-shot learning event detection. arXiv preprint arXiv:2006.10093 (2020)","DOI":"10.18653\/v1\/2020.nuse-1.5"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Liu, J., Chen, Y., Liu, K., Bi, W., Liu, X.: Event extraction as machine reading comprehension. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1641\u20131651 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.128"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Cong, X., Cui, S., Yu, B., Liu, T., Wang, Y., Wang, B.: Few-shot event detection with prototypical amortized conditional random field. arXiv preprint arXiv:2012.02353 (2020)","DOI":"10.18653\/v1\/2021.findings-acl.3"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Cui, S., Yu, B., Liu, T., Zhang, Z., Wang, X., Shi, J.: Edge-enhanced graph convolution networks for event detection with syntactic relation. arXiv preprint arXiv:2002.10757 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.211"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Deng, S., Zhang, N., Kang, J., Zhang, Y., Zhang, W., Chen, H.: Meta-learning with dynamic-memory-based prototypical network for few-shot event detection. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 151\u2013159 (2020)","DOI":"10.1145\/3336191.3371796"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Maven: a massive general domain event detection dataset. arXiv preprint arXiv:2004.13590 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.129"},{"issue":"3","key":"23_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: A survey on few-shot learning. ACM comput. Surv. (CSUR) 53(3), 1\u201334 (2020)","journal-title":"ACM comput. Surv. (CSUR)"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Xue, W., Wang, W.: One-shot image classification by learning to restore prototypes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 6558\u20136565 (2020)","DOI":"10.1609\/aaai.v34i04.6130"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Deng, S., et la.: OntoED: low-resource event detection with ontology embedding. arXiv preprint arXiv:2105.10922 (2021)","DOI":"10.18653\/v1\/2021.acl-long.220"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Chen, J., Lin, H., Han, X., Sun, L.: Honey or poison? solving the trigger curse in few-shot event detection via causal intervention. arXiv preprint arXiv:2109.05747 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.637"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Lai, V., Dernoncourt, F., Nguyen, T.H.: Learning prototype representations across few-shot tasks for event detection. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.427"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Lai, V.D., Nguyen, M.V., Nguyen, T.H., Dernoncourt, F.: Graph learning regularization and transfer learning for few-shot event detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2172\u20132176 (2021)","DOI":"10.1145\/3404835.3463054"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Shen, S., Wu, T., Qi, G., Li, Y.F., Haffari, G., Bi, S.: Adaptive knowledge-enhanced Bayesian meta-learning for few-shot event detection. arXiv preprint arXiv:2105.09509 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.214"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Veyseh, A.P.B., Van Nguyen, M., Trung, N.N., Min, B., Nguyen, T.H.: Modeling document-level context for event detection via important context selection. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 5403\u20135413 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.439"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Zheng, J., Cai, F., Chen, W., Lei, W., Chen, H.: Taxonomy-aware learning for few-shot event detection. In: Proceedings of the Web Conference 2021, pp. 3546\u20133557 (2021)","DOI":"10.1145\/3442381.3449949"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Liu, J., Chen, Y., Xu, J.: Saliency as evidence: event detection with trigger saliency attribution. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4573\u20134585 (2022)","DOI":"10.18653\/v1\/2022.acl-long.313"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Li, Q., et al.: A survey on deep learning event extraction: approaches and applications. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3213168"},{"key":"23_CR32","unstructured":"Ji, B., Li, S., Gan, S., Yu, J., Ma, J., Liu, H.: Few-shot named entity recognition with entity-level prototypical network enhanced by dispersedly distributed prototypes. arXiv preprint arXiv:2208.08023 (2022)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44696-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T09:00:37Z","timestamp":1730278837000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44696-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031446955","9783031446962"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44696-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Foshan","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":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2023\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"478","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":"143","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":"30% - 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":"3","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}