Research on the Relationship between Emergent Event Extraction and Evolution— The Case of the Emergency Services Platform
Zeng Jin1,2,3, Jiang Changjiang1, Li Xinlai2,3, Chen Ling2,3
1.School of Information Management, Wuhan University, Wuhan 430072 2.School of Information Management, Hubei University of Economics, Wuhan 430205 3.Institute of Big Data and Digital Economy, Hubei University of Economics, Wuhan 430205
曾金, 江长江, 李新来, 陈玲. 突发事件抽取与演化关系研究——以“应急服务网”为例[J]. 情报学报, 2024, 43(11): 1334-1348.
Zeng Jin, Jiang Changjiang, Li Xinlai, Chen Ling. Research on the Relationship between Emergent Event Extraction and Evolution— The Case of the Emergency Services Platform. 情报学报, 2024, 43(11): 1334-1348.
1 柯平. 应急知识管理: 理论基础、研究领域与应用前景[J]. 信息资源管理学报, 2021, 11(1): 8-16. 2 习近平在中央政治局第十九次集体学习时强调 充分发挥我国应急管理体系特色和优势 积极推进我国应急管理体系和能力现代化[EB/OL]. (2019-11-30). http://www.xinhuanet.com/politics/leaders/2019-11/30/c_1125292909.htm. 3 姜卉, 黄钧. 罕见重大突发事件应急实时决策中的情景演变[J]. 华中科技大学学报(社会科学版), 2009, 23(1): 104-108. 4 曾大军, 曹志冬. 突发事件态势感知与决策支持的大数据解决方案[J]. 中国应急管理, 2013(11): 15-23. 5 范维澄. 国家突发公共事件应急管理中科学问题的思考和建议[J]. 中国科学基金, 2007, 21(2): 71-76. 6 赵志耘. 论复杂信息环境下的科技情报卓智赋能[J]. 情报学报, 2022, 41(12): 1229-1237. 7 张海涛, 张春龙, 栾宇, 等. 哲学视角的重大突发事件图景建构研究[J]. 情报学报, 2022, 41(12): 1294-1304. 8 Chen C, Vincent N G. Joint modeling for Chinese event extraction with rich linguistic features[C]// Proceedings of the 24th International Conference on Computational Linguistics. Stroudsburg: Association of Computational Linguistics, 2012: 529-544. 9 Ji H, Grishman R. Refining event extraction through cross-document inference[C]// Proceedings of the50th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association of Computational Linguistics, 2012: 835-844. 10 尹浩然. 基于深度学习的突发事件识别研究[D]. 北京: 中国人民公安大学, 2021. 11 Riloff E. Automatically constructing a dictionary for information extraction tasks[C]// Proceedings of the Eleventh National Conference on Artificial Intelligence. Palo Alto: AAAI Press, 1993: 811-816. 12 赵文娟, 刘忠宝, 王永芳. 基于依存句法分析的事件角色填充研究[J]. 情报科学, 2017, 35(7): 65-69. 13 赵军磊. 突发事件网络信息抽取和可视化技术研究[D]. 泉州: 华侨大学, 2019. 14 Gupta M, Gupta P. Research and implementation of event extraction from twitter using LDA and scoring function[J]. International Journal of Information Technology, 2019, 11(2): 365-371. 15 Zheng S, Cao W, Xu W, et al. Doc2EDAG: an end-to-end document-level framework for Chinese financial event extraction[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2019: 337-346. 16 闻畅, 刘宇, 顾进广. 基于注意力机制的双向长短时记忆网络模型突发事件演化关系抽取[J]. 计算机应用, 2019, 39(6): 1646-1651. 17 孙小川. 面向微博的突发事件抽取方法研究[D]. 北京: 中国人民公安大学, 2020. 18 石磊, 李敬明, 朱家明. 基于BERT-BiLSTM-CRF的突发公共卫生事件抽取研究[J]. 哈尔滨师范大学自然科学学报, 2022, 38(2): 37-42. 19 李明亮. 基于深度学习的中文突发事件的抽取算法研究[D]. 广州: 广东工业大学, 2022. 20 李正民. 突发事件事理图谱的构建及应用研究[D]. 青岛: 青岛大学, 2022. 21 吴小兰, 章成志. 基于DTM-LPA的突发事件话题演化方法研究——以H7N9微博为例[J]. 图书与情报, 2015(3): 9-16. 22 吴小兰, 章成志. 基于突发事件特征网络的用户社区发现与社区主题演化研究——以新浪微博H7N9事件为例[J]. 情报理论与实践, 2017, 40(5): 94-98, 60. 23 蒋珊. 基于微博的事件演化分析及摘要抽取方法研究[D]. 成都: 电子科技大学, 2019. 24 Arif M, Khan F, Ahmed S, et al. Evolving extreme events caused by climate change: a tail based Bayesian approach for extreme event risk analysis[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2021, 235(6): 963-972. 25 孟金凤. 突发事件抽取和演化关键技术研究[D]. 成都: 电子科技大学, 2021. 26 杨可硕. 地铁突发事件情景构建与演化分析[D]. 石家庄: 石家庄铁道大学, 2022. 27 刘桐. 基于新闻文本摘要的突发事件演化分析[D]. 成都: 电子科技大学, 2022. 28 中文突发事件语料库[EB/OL]. https://github.com/shijiebei2009/CEC-Corpus. 29 应急服务网[EB/OL]. http://www.52safety.com. 30 Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[C]// Proceedings of the 27th Annual Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2013: 3136-3144. 31 Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2019: 4171-4186. 32 Zhang S, Zheng D Q, Hu X C, et al. Bidirectional long short-term memory networks for relation classification[C]// Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, 2015: 73-78. 33 Kim Y. Convolutional neural networks for sentence classification[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1746-1751. 34 刘炜, 王旭, 张雨嘉, 等. 一种面向突发事件的文本语料自动标注方法[J]. 中文信息学报, 2017, 31(2): 76-85. 35 朱文跃, 刘宗田. 基于事件本体的突发事件领域知识建模[J]. 计算机工程与应用, 2018, 54(21): 148-155. 36 Reimers N, Gurevych I. Optimal hyperparameters for deep LSTM-networks for sequence labeling tasks[OL]. (2017-08-16). https://arxiv.org/pdf/1707.06799. 37 谢腾, 杨俊安, 刘辉. 基于BERT-BiLSTM-CRF模型的中文实体识别[J]. 计算机系统应用, 2020, 29(7): 48-55. 38 史培军. 三论灾害研究的理论与实践[J]. 自然灾害学报, 2002, 11(3): 1-9. 39 姜波, 张超, 陈涛, 等. 基于Bayes网络的暴雨情景构建和演化方法[J]. 清华大学学报(自然科学版), 2021, 61(6): 509-517. 40 Lan Z Z, Chen M D, Goodman S, et al. ALBERT: a lite BERT for self-supervised learning of language representations[C/OL]//Proceedings of International Conference on Learning Representations. Appleton: ICLR, 2020-02-09. https://arxiv.org/pdf/1909.11942v6. 41 Liu Y H, Ott M, Goyal N, et al. RoBERTa: a robustly optimized BERT pretraining approach[OL]. (2019-07-26). https://arxiv.org/pdf/1907.11692.
