{"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/exploiting-contextual-information-via-dynamic","title":"Exploiting Contextual Information via Dynamic Memory Network for Event Detection","arxiv_id":"1810.03449","date":"2018-10-03","proceeding":"EMNLP 2018 10","authors":["Shaobo Liu","Rui Cheng","Xiaoming Yu","Xue-Qi Cheng"],"abstract":"The task of event detection involves identifying and categorizing event\ntriggers. Contextual information has been shown effective on the task. However,\nexisting methods which utilize contextual information only process the context\nonce. We argue that the context can be better exploited by processing the\ncontext multiple times, allowing the model to perform complex reasoning and to\ngenerate better context representation, thus improving the overall performance.\nMeanwhile, dynamic memory network (DMN) has demonstrated promising capability\nin capturing contextual information and has been applied successfully to\nvarious tasks. In light of the multi-hop mechanism of the DMN to model the\ncontext, we propose the trigger detection dynamic memory network (TD-DMN) to\ntackle the event detection problem. We performed a five-fold cross-validation\non the ACE-2005 dataset and experimental results show that the multi-hop\nmechanism does improve the performance and the proposed model achieves best\n$F_1$ score compared to the state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1810.03449v1","url_pdf":"http://arxiv.org/pdf/1810.03449v1.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":"exploiting-contextual-information-via-dynamic","repo_url":"https://github.com/AveryLiu/TD-DMN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"event-detection","task_name":"Event Detection"}],"methods":[{"method_slug":"dynamic-memory-network","method_name":"Dynamic Memory Network"},{"method_slug":"gru","method_name":"GRU"},{"method_slug":"memory-network","method_name":"Memory Network"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.03449","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}