{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:10:09Z","timestamp":1721434209789},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"7","funder":[{"name":"Youth Fund for Humanities and Social Science Research of Ministry of Education of China","award":["21YJCZH064"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"The dependency syntactic structure is widely used in event extraction. However, the dependency structure reflecting syntactic features is essentially different from the event structure that reflects semantic features, leading to the performance degradation. In this article, we propose to use Event Trigger Structure for Event Extraction (ETSEE), which can compensate the inconsistency between two structures. First, we leverage the ACE2005 dataset as case study, and annotate three kinds of ETSs, that is, \u201clight verb + trigger\u201d, \u201cpreposition structures\u201d and \u201ctense + trigger\u201d. Then we design a graph-based event extraction model that jointly identifies triggers and arguments, where the graph consists of both the dependency structure and ETSs. Experiments show that our model significantly outperforms the state-of-the-art methods. Through empirical analysis and manual observation, we find that the ETSs can bring the following benefits: (1) enriching trigger identification features by introducing structural event information; (2) enriching dependency structures with event semantic information; (3) enhancing the interactions between triggers and candidate arguments by shortening their distances in the dependency graph.<\/jats:p>","DOI":"10.1145\/3663567","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T10:53:35Z","timestamp":1715079215000},"page":"1-18","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Chinese Event Extraction with Event Trigger Structures"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1816-1761","authenticated-orcid":false,"given":"Fei","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0001-1782-858X","authenticated-orcid":false,"given":"Kaifang","family":"Deng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0001-9626-593X","authenticated-orcid":false,"given":"Yiwen","family":"Mo","sequence":"additional","affiliation":[{"name":"School of International Education, Wuhan University, Wuhan, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0007-3628-0218","authenticated-orcid":false,"given":"Yuanze","family":"Ji","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0008-6543-2548","authenticated-orcid":false,"given":"Chong","family":"Teng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9613-5927","authenticated-orcid":false,"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"529","volume-title":"COLING 2012, 24th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 8-15 December 2012","author":"Chen Chen","year":"2012","unstructured":"Chen Chen and Vincent Ng. 2012. Joint modeling for chinese event extraction with rich linguistic features. In COLING 2012, 24th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 8-15 December 2012, Martin Kay and Christian Boitet (Eds.). Indian Institute of Technology Bombay, 529\u2013544. 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In Proceedings of the 4th International Conference on Language Resources and Evaluation, LREC 2004, May 26-28, 2004, Lisbon, Portugal. European Language Resources Association. Retrieved from DOI:http:\/\/www.lrec-conf.org\/proceedings\/lrec2004\/summaries\/5.htm"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.49"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.361"},{"key":"e_1_3_3_11_2","unstructured":"Jun Gao Huan Zhao Changlong Yu and Ruifeng Xu. 2023. Exploring the feasibility of ChatGPT for event extraction. arXiv:2303.03836. Retrieved from https:\/\/arxiv.org\/abs\/2303.03836"},{"key":"e_1_3_3_12_2","unstructured":"Ralph Grishman David Westbrook and Adam Meyers. 2005. Nyu\u2019s english ACE 2005 system description. 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