{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:29:54Z","timestamp":1765232994941,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030630300"},{"type":"electronic","value":"9783030630317"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63031-7_13","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:05:35Z","timestamp":1605139535000},"page":"174-183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Novel Joint Framework for Multiple Chinese Events Extraction"],"prefix":"10.1007","author":[{"given":"Nuo","family":"Xu","sequence":"first","affiliation":[]},{"given":"Haihua","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Dongyan","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ji, H.: Language specific issue and feature exploration in Chinese event extraction. In: Proceedings of NAACL-HLT 2009, pp. 209\u2013212 (2009)","DOI":"10.3115\/1620853.1620910"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Lin, H.Y., et al.: Nugget proposal networks for Chinese event detection. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1565\u20131574 (2018)","DOI":"10.18653\/v1\/P18-1145"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: Exploring pre-trained language models for event extraction and generation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5284\u20135294 (2019)","DOI":"10.18653\/v1\/P19-1522"},{"key":"13_CR4","unstructured":"Doddington, G.R., et al.: The automatic content extraction (ACE) program-tasks, data, and evaluation. In: LREC (2004)"},{"issue":"4","key":"13_CR5","first-page":"1015","volume":"30","author":"RF He","year":"2019","unstructured":"He, R.F., Duan, S.Y.: Joint Chinese event extraction based multi-task learning. J. Softw. 30(4), 1015\u20131030 (2019)","journal-title":"J. Softw."},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Liu, X., Luo, Z., Huang, H.: Jointly multiple events extraction via attention-based graph information aggregation. arXiv preprint arXiv:1809.09078 (2018)","DOI":"10.18653\/v1\/D18-1156"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Cho, K., Grishman, R.: Joint event extraction via recurrent neural networks. In: Proceedings of NAACL-HLT 2016, pp. 300\u2013309 (2016)","DOI":"10.18653\/v1\/N16-1034"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Nguyen, T.M., Nguyen, T.H.: One for all: neural joint modeling of entities and events. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6851\u20136858 (2019)","DOI":"10.1609\/aaai.v33i01.33016851"},{"key":"13_CR9","unstructured":"Chen, C., Ng, V.: Joint modeling for Chinese event extraction with rich linguistic features. In: Proceedings of COLING 2012, pp. 529\u2013544 (2012)"},{"key":"13_CR10","unstructured":"Liao, S., Grishman, R.: Using document level cross-event inference to improve event extraction. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 789\u2013797 (2010)"},{"key":"13_CR11","unstructured":"Li, Qi., Ji, H., Huang, L.: Joint event extraction via structured prediction with global features. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 73\u201382 (2013)"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Y.B., et al.: 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, vol.1, pp. 167\u2013176 (2015)","DOI":"10.3115\/v1\/P15-1017"},{"key":"13_CR13","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-319-50496-4_23","volume-title":"Natural Language Understanding and Intelligent Applications","author":"Y Zeng","year":"2016","unstructured":"Zeng, Y., Yang, H., Feng, Y., Wang, Z., Zhao, D.: A convolution BiLSTM neural network model for Chinese event extraction. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL\/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 275\u2013287. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50496-4_23"},{"key":"13_CR14","unstructured":"McCann, B., et al.: Learned in translation: contextualized word vectors. In Advances in Neural Information Processing Systems, pp. 6294\u20136305 (2017)"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, vol. 1, pp. 2227\u20132237 (2018)","DOI":"10.18653\/v1\/N18-1202"},{"key":"13_CR16","unstructured":"Radford, A., et al.: Improving language understanding by generative pre-training (2018). https:\/\/www.cs.ubc.ca\/amuham01\/LING530\/papers\/radford2018improving.pdf"},{"key":"13_CR17","unstructured":"Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Sha, L., et al.: Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 5916\u20135923 (2018)","DOI":"10.1609\/aaai.v32i1.12034"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63031-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:01:58Z","timestamp":1710270118000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63031-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030630300","9783030630317"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63031-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hainan","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cips-cl.org\/static\/CCL2020\/index.html","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":"www.softconf.com","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","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":"32","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":"2","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":"32% - 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":"3","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)"}}]}}