基于时空分析的突发事件检测方法

doi:10.19678/j.issn.1000-3428.0046728

计算机工程 ›› 2018, Vol. 44 ›› Issue (5): 7-13. doi: 10.19678/j.issn.1000-3428.0046728

基于时空分析的突发事件检测方法

梁月仙 ^1,2,3,陈自岩 ^1,2,王洋^1,2,张跃 ^1,2,3,郭智 ^1,2

1.中国科学院空间信息处理与应用系统技术重点实验室,北京 100190; 2.中国科学院电子学研究所,北京 100190; 3.中国科学院大学,北京 100190

收稿日期:2017-04-10 出版日期:2018-05-15 发布日期:2018-05-15
作者简介:梁月仙(1991—),女,硕士,主研方向为文本数据挖掘;陈自岩、王洋,助理研究员;张跃,博士;郭智,研究员。
基金资助:
国家自然科学基金(41501485)。

Bursty Event Detection Method Based on Spatio-temporal Analysis

LIANG Yuexian^1,2,3,CHEN Ziyan ^1,2,WANG Yang ^1,2,ZHANG Yue ^1,2,3,GUO Zhi ^1,2

1.Key Laboratory of Technology in Geo-spatial Information Processing and Application System,Chinese Academy of Sciences,Beijing 100190,China; 2.Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China; 3.University of Chinese Academy of Sciences,Beijing 100190,China

Received:2017-04-10 Online:2018-05-15 Published:2018-05-15

摘要/Abstract

摘要：

现有突发事件检测方法多数未考虑事件的重要性,且以孤立的方式看待事件的突发时间域和空间域。为此,提出一种基于时空要素综合分析的突发事件检测方法。引入数据立方体结构存储事件词,通过基于语义相似性的实时事件聚类算法抽取出重要事件。根据TFIDF计算事件在时空维度上的出现权重,给出有限状态机-高斯分布模型识别时空突发事件。实验结果表明,该方法能够有效地识别出事件的突发时间段和突发区域,与现有突发事件检测方法相比,检测突发事件的准确率更高。

关键词: 突发事件, 时空分析, 事件抽取, 实时事件聚类, 数据立方体

Abstract:

The existing bursty event detection method does not consider the importance of the eveuts,and treats the bursty event time domain and spatial domain of the incident in an isolated manner,and proposes an incident detection method based on comprehensive analysis of spatio-temporal elements.The data cube structure is introduced to store event words,and important events are extracted by a real-time event clustering algorithm based on semantic similarity.TFIDF is used to calculate the occurrence weights of events in the space-time dimension,and the finite state machine-Gaussian distribution model is used to identify spatio-temporal events.Experimental results show that the method can effectively identify bursty time and bursty area of the event,compared with the existing emergency detection method,the accuracy of detecting eveuts is higher.

Key words: bursty event, spatio-temporal analysis, event extraction, real-time event clustering, data cube

中图分类号:

TP391

梁月仙,陈自岩,王洋,张跃,郭智. 基于时空分析的突发事件检测方法[J]. 计算机工程, 2018, 44(5): 7-13.

LIANG Yuexian,CHEN Ziyan,WANG Yang,ZHANG Yue,GUO Zhi. Bursty Event Detection Method Based on Spatio-temporal Analysis[J]. Computer Engineering, 2018, 44(5): 7-13.

https://www.ecice06.com/CN/Y2018/V44/I5/7

参考文献

［1］BETHART S,MARTIN J H.Identification of event mentions and their semantic class［C］//Proceedings of Conference on Empirical Methods in Natural Language Processing.Sydney,Australia:［s.n.］,2006:146-154.
［2］LI P,ZHOU G,ZHU Q.Minimally supervised Chinese event extraction from multiple views［J］.ACM Transactions on Asian and Low-resource Language Information Processing,2016,6(2):13.
［3］NGUYEN M T,NGUYEN T T.Extraction of disease events for a real-time monitoring system［C］//Proceedings of Symposium on Information and Communication Technology.Washington D.C.,USA:IEEE Press,2013:139-147.
［4］侯立斌,李培峰,朱巧明.基于CRFs和跨事件的事件识别研究［J］.计算机工程,2012,38(24):191-195.
［5］TSOLMON B,LEE K S.An event extraction model based on timeline and user analysis in latent dirichlet allocation［M］.New York,USA:ACM Press,2014.
［6］SILVA J D A,HRUSCHKA E R.A support system for clustering data streams with a variable number of clusters［J］.ACM Transactions on Autonomous & Adaptive Systems,2016,11(2):11.
［7］LIN C X,ZHAO B,MEI Q.PET:a statistical model for popular events tracking in social communities［C］//Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2010:929-938.
［8］DIAO Q,JIANG J,ZHU F,et al.Finding bursty topics from microblogs［C］//Proceedings of Association for Computational Linguistics.［S.1.］:Association for Computational Linguistics,2012:536-544.
［9］KLEINBERG J.Bursty and hierarchical structure in streams［J］.Data Mining & Knowledge Discovery,2003,7(4):373-397.
［10］LAPPAS T,ARAI B,PLATAKIS M,et al.On burstiness-aware search for document sequences［C］//Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2009:477-486.
［11］ALVES R A D S,ASSUNCAO R M,STANCIOLI V D M P O.Burstiness scale:a parsimonious model for characterizing random series of events［C］//Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York,USA:ACM Press,2016:1405-1414.
［12］KALOGERATOS A,ZAGORISIOS P,LIKAS A.Improving text stream clustering using term burstiness and co-burstiness［C］//Proceedings of Hellenic Conference on Artificial Intelligence.Athens,Hellenic:［s.n.］,2016:1-9.
［13］ZHAO L,CHEN F,LU C T,et al.Online spatial event forecasting in microblogs［J］.ACM Transactions on Spatial Algorithms & Systems,2016,2(4):15.
［14］SCHUBERT E,WEILER M,KRIEGEL H P.SPOTHOT:scalable detection of geo-spatial events in large textual streams［C］//Proceedings of International Conference on Scientific & Statistical Database Management.Washington D.C.,USA:IEEE Press,2016:1-12.
［15］QUEZADA M,POBLETE B.Location-aware model for news events in social media［C］//Proceedings of International ACM SIGIR Conference.New York,USA:ACM Press,2015:935-938.
［16］LAPPAS T,VIEIRA M R,GUNOPULOS D,et al.On the spatiotemporal burstiness of terms［J］.Proceedings of the VLDB Endowment,2012,5(9).
［17］TAMURA K,MATSUI T,KITAKAMI H,et al.Identifying local temporal burstiness using MACD histogram［C］//Proceedings of IEEE International Conference on Systems,Man,and Cybernetics.Washington D.C.,USA:IEEE Press,2015:2666-2671.
［18］DODDINGTON G,MITCHELL A,PRZYBOCKI M,et al.The automatic content extraction program-tasks,data,and evaluation［C］//Proceedings of LREC’04.Washington D.C.,USA:IEEE Press,2004:158-165.
［19］PUSTEJOVSKY J,HANKS P,SAURI R,et al.The timebank corpus［C］//Proceedings of Corpus Linguistics Conference.Washington D.C.,USA:IEEE Press,2003:215-222.
［20］LAFFERTY J D,MCCALLUM A,PEREIRA F C N.Conditional random fields:probabilistic models for segmenting and labeling sequence Data［J］.Machine Learning 2002,3(2):282-289.
［21］马雷雷,李宏伟,连世伟,等.地名知识辅助的中文地名消歧方法［J］.地理与地理信息科学,2016,32(4):5-10.
［22］SILVA J A,FARIA E R,BARROS R C,et al.Data stream clustering:a survey［J］.ACM Computing Surveys,2014,46(1):13.
［23］蔡偃武.面向大规模数据的在线新事件检测［D］.上海:华东理工大学,2014.
［24］YIN J,WANG J.A text clustering algorithm using an online clustering scheme for initialization［C］//Proceedings of ACM SIGKDD International Conference.New York,USA:ACM Press,2016:1995-2004.
［25］PENNINGTON J,SOCHER R,MANNING C.Glove:global vectors for word representation［C］//Proceedings of Conference on Empirical Methods in Natural Language Processing.Washington D.C.,USA:IEEE Press,2014:1532-1543.
［26］MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space［EB/OL］.［2013-01-12］.https://www.mendeley.com.
［27］FENG W,ZHANG C,ZHANG W,et al.STREAMCUBE:hierarchical spatio-temporal hashtag clustering for event exploration over the twitter stream［C］//Proceedings of IEEE International Conference on Data Engineering.Washington D.C.,USA:IEEE Press,2015:1561-1572.
［28］BLER D M,LAFFERTY J D.Dynamic topic models［C］//Proceedings of DBLP’06.Washington D.C.,USA:IEEE Press,2006:113-120.

[1]	李鸿鹏, 马博, 杨雅婷, 王磊, 王震, 李晓. 基于槽位语义增强提示学习的篇章级事件抽取方法[J]. 计算机工程, 2023, 49(9): 23-31.
[2]	杨红菊, 靳新宇. 一个实体关系与事件抽取的通用模型[J]. 计算机工程, 2023, 49(2): 143-149.
[3]	苗佳, 段跃兴, 张月琴, 张泽华. 基于CNN-BiGRU模型的事件触发词抽取方法[J]. 计算机工程, 2021, 47(9): 69-74,83.
[4]	郑巧夺, 吴贞东, 邹俊颖. 基于双层CNN-BiGRU-CRF的事件因果关系抽取[J]. 计算机工程, 2021, 47(5): 58-64,72.
[5]	蔚莹, 殷雁君. 突发事件人群聚集场所的谣言传播模型[J]. 计算机工程, 2019, 45(9): 302-309,315.
[6]	柳亦婷, 李培峰. 基于局部实体特征的事件触发词抽取[J]. 计算机工程, 2019, 45(11): 213-217,224.
[7]	许硕, 唐作其, 王鑫. 基于D-AHP与TOPSIS的突发事件应急管理能力评估[J]. 计算机工程, 2019, 45(10): 314-320.
[8]	陈斌,周勇,刘兵. 基于卷积双向长短期记忆网络的事件触发词抽取[J]. 计算机工程, 2019, 45(1): 153-158.
[9]	王洋,游进国,张婷,张正凡. 数据立方体格的图结构特性研究[J]. 计算机工程, 2017, 43(2): 68-73.
[10]	侯立斌, 李培峰, 朱巧明. 基于CRFs和跨事件的事件识别研究[J]. 计算机工程, 2012, 38(24): 191-195.
[11]	陈富强, 奚建清. 浓缩商覆盖立方体技术研究[J]. 计算机工程, 2011, 37(7): 59-61.
[12]	黄晓森, 彭利宁, 陈启买. 基于数据立方体的动态推理控制方法[J]. 计算机工程, 2011, 37(17): 32-34,37.
[13]	郑魁;疏学明;袁宏永. 网络舆情热点信息自动发现方法[J]. 计算机工程, 2010, 36(3): 4-6.
[14]	张应龙, 盛立琨, 杨乐. 压缩数据集上的快速Cube计算方法[J]. 计算机工程, 2010, 36(12): 85-87.
[15]	周海晴;陈启买;刘海. 基于数据立方体的数据仓库安全控制[J]. 计算机工程, 2010, 36(10): 152-154.

选择文件类型/文献管理软件名称

选择包含的内容

基于时空分析的突发事件检测方法

Bursty Event Detection Method Based on Spatio-temporal Analysis

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于时空分析的突发事件检测方法

Bursty Event Detection Method Based on Spatio-temporal Analysis

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价