{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T23:37:36Z","timestamp":1776296256235,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>The task of event extraction contains subtasks including detections for entity mentions, event triggers and argument roles. Traditional methods solve them as a pipeline, which does not make use of task correlation for their mutual benefits. There have been recent efforts towards building a joint model for all tasks. However, due to technical challenges, there has not been work predicting the joint output structure as a single task. We build a first model to this end using a neural transition-based framework, incrementally predicting complex joint structures in a state-transition process. Results on standard benchmarks show the benefits of the joint model, which gives the best result in the literature.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/753","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"5422-5428","source":"Crossref","is-referenced-by-count":32,"title":["Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model"],"prefix":"10.24963","author":[{"given":"Junchi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer, Wuhan University, China"}]},{"given":"Yanxia","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, China"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University, China"},{"name":"Institute of Advanced Technology, Westlake Institute for Advanced Study, China"}]},{"given":"Mengchi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, Wuhan University, China"}]},{"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, China"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:51:35Z","timestamp":1564300295000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/753"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/753","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}