{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jointly-multiple-events-extraction-via","title":"Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation","arxiv_id":"1809.09078","date":"2018-09-24","proceeding":"EMNLP 2018 10","authors":["Xiao Liu","Zhunchen Luo","He-Yan Huang"],"abstract":"Event extraction is of practical utility in natural language processing. In\nthe real world, it is a common phenomenon that multiple events existing in the\nsame sentence, where extracting them are more difficult than extracting a\nsingle event. Previous works on modeling the associations between events by\nsequential modeling methods suffer a lot from the low efficiency in capturing\nvery long-range dependencies. In this paper, we propose a novel Jointly\nMultiple Events Extraction (JMEE) framework to jointly extract multiple event\ntriggers and arguments by introducing syntactic shortcut arcs to enhance\ninformation flow and attention-based graph convolution networks to model graph\ninformation. The experiment results demonstrate that our proposed framework\nachieves competitive results compared with state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1809.09078v2","url_pdf":"http://arxiv.org/pdf/1809.09078v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"jointly-multiple-events-extraction-via","repo_url":"https://github.com/lx865712528/JMEE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"jointly-multiple-events-extraction-via","repo_url":"https://github.com/Tuofengalways/ee_Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"jointly-multiple-events-extraction-via","repo_url":"https://github.com/nlpcl-lab/bert-event-extraction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"event-extraction","task_name":"Event Extraction"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.09078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.09078"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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