{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T03:18:37Z","timestamp":1762053517843,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["4222022"],"award-info":[{"award-number":["4222022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the rapid development of the internet and social media, extracting emergency events from online news reports has become an urgent need for public safety. However, current studies on the text mining of emergency information mainly focus on text classification and event recognition, only obtaining a general and conceptual cognition about an emergency event, which cannot effectively support emergency risk warning, etc. Existing event extraction methods of other professional fields often depend on a domain-specific, well-designed syntactic dependency or external knowledge base, which can offer high accuracy in their professional fields, but their generalization ability is not good, and they are difficult to directly apply to the field of emergency. To address these problems, an end-to-end Chinese emergency event extraction model, called EmergEventMine, is proposed using a deep adversarial network. Considering the characteristics of Chinese emergency texts, including small-scale labelled corpora, relatively clearer syntactic structures, and concentrated argument distribution, this paper simplifies the event extraction with four subtasks as a two-stage task based on the goals of subtasks, and then develops a lightweight heterogeneous joint model based on deep neural networks for realizing end-to-end and few-shot Chinese emergency event extraction. Moreover, adversarial training is introduced into the joint model to alleviate the overfitting of the model on the small-scale labelled corpora. Experiments on the Chinese emergency corpus fully prove the effectiveness of the proposed model. Moreover, this model significantly outperforms other existing state-of-the-art event extraction models.<\/jats:p>","DOI":"10.3390\/ijgi11060345","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T10:25:12Z","timestamp":1654856712000},"page":"345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["EmergEventMine: End-to-End Chinese Emergency Event Extraction Using a Deep Adversarial Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Jianzhuo","family":"Yan","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihong","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongchuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9246-8273","authenticated-orcid":false,"given":"Hongxia","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingcai","family":"Gao","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunpeng","family":"Cao","sequence":"additional","affiliation":[{"name":"Beijing Research Institute of Smart Water, Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of MRI and Brain Informatics, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102024","DOI":"10.1016\/j.ijdrr.2020.102024","article-title":"CrowdEIM: Crowdsourcing Emergency Information Management Tasks to Mobile Social Media Users","volume":"54","author":"Shen","year":"2021","journal-title":"Int. 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