面向公共安全事件的网络文本大数据结构化研究

doi:10.12082/dqxxkx.2019.180680

Abstract

Abstract:

The information of public security event contained in text can be the data source of the evaluation and the relief if it can be structured into a relational database. Although previous research can extract the information of events into different attributes, the determination on the attribution of the attribute information to specific event remains unsolved. To solve the problem, this paper proposes a theoretical frame of public security event themed web text structuring, which is composed of three parts. First, an event semantic model is used to construct the seismic event semantic framework which defines abstract elements of event and their semantic relationships. Taking seismicity as an example, spatial element, time element, attribute element, source element are defined as basic elements. Spatial element includes earthquake latitude, longitude, depth and location. Attribute element is further subdivided into four sub-elements: Cause, result, behavior and influence element. Next, an annotation system is applied to typical event materials to label semantic elements, e.g. the place name where an earthquake took place, that is, instantiation of the abstract elements. The key to this step is labeling the relations between elements and specific event. Finally, the event text is structured into event type, event name, event time, event location and other attributes by using the text information extraction algorithm. The algorithm used the labeled materials in the last step as training data to optimize parameters, which can incorporate linked information. The extracted event text (e.g. words, phrases) finally is normalized to structured information for further analysis. An event information mining platform following the whole frame is developed, which includes the modules of webpage searching, text cleaning, event information extraction, visualization and analyzing. The platform processed the whole Chinese webpages of 2014 and found 85 506 seismicity reports. Taking Yunnanludian earthquake as an example, we display the structuring process and result of related web text, which can be the important reference for the relief of the disaster and the analysis of public concern. With the platform, we can demonstrate the seismic text structuring result and its social concern across China, which can be a new tool of event information mining and analyzing.

Key words: semantic framework, text parsing, social concern about events, seismic events, spatial search engine

Tao PEI, Sihui GUO, Yecheng YUAN, Xueying ZHANG, Wen YUAN, Ang GAO, Zhiyuan ZHAO, Cunjin XUE. Public Security Event Themed Web Text Structuring[J].Journal of Geo-information Science, 2019, 21(1): 2-13.DOI:10.12082/dqxxkx.2019.180680

Figures/Tables 10

Tab. 1

Fig. 1

Tab. 2

Tab. 3

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Tab.4

Fig. 6

References 61

[1]	Sakaki T, Okazaki M, Matsuo Y.Earthquake shakes Twitter users: Real-time event detection by social sensors[C]. Raleigh: International Conference on World Wide Web, 2010:851-860.
[2]	仇培元,陆锋,张恒才,等.蕴含地理事件微博客消息的自动识别方法[J].地球信息科学学报,2016,18(7):886-893. doi: 10.3724/SP.J.1047.2016.00886
	[ Qiu P Y, Lu F, Zhang H C, et al.Automatic identification method of micro-blog messages containing geographical events[J]. Journal of Geo-information Science, 2016,18(7):886-893. ] doi: 10.3724/SP.J.1047.2016.00886
[3]	袁烨城,刘海江,裴韬,等.基于语义知识的空间关系识别研究[J].地球信息科学学报,2014,16(5):681-690. doi: 10.3724/SP.J.1047.2014.00681
	[ Yuan Y C, Liu H J, Pei T, et al.Spatial relation extraction from Chinese characterized documents based on semantic knowledge[J]. Journal of Geo-Information Science, 2014,16(5):681-690. ] doi: 10.3724/SP.J.1047.2014.00681
[4]	余丽,陆锋,张恒才.网络文本蕴涵地理信息抽取:研究进展与展望[J].地球信息科学学报,2015,17(2):127-134. doi: 10.3724/SP.J.1047.2015.00127
	[ Yu L, Lu F, Zhang H C.Extracting geographic information from web texts: Status and development[J]. Journal of Geo-information Science, 2015,17(2):127-134. ] doi: 10.3724/SP.J.1047.2015.00127
[5]	Rafea A, Mostafa N A.Topic extraction in social media[C]. San Diego: International conference on collaboration technologies and systems, 2013:94-98.
[6]	Petkos G, Papadopoulos S, Aiello L, et al.A soft frequent pattern mining approach for textual topic detection[C]. Thessaloniki: International conference on web intelligence, Mining and Semantics, 2014:1-10.
[7]	谭红叶,郑家恒,刘开瑛.中国地名的自动识别方法研究[C].北京:全国计算机语言联合学术会议,1999.
	[ Tan H Y, Zheng J H, Liu K Y.Chinese place name automatic recognition[C]. Beijing: National Academic Conference on computer languages, 1999. ]
[8]	肖计划. 地名识别与匹配的概率统计方法[J].测绘科学技术学报,2014,31(4):408-412. doi: 10.3969/j.issn.1673-6338.2014.04.017
	[ Xiao J H.Method of recognition and match of place name based on statistic[J]. Journal of Geomatics Science and Technology, 2014,31(4):408-412. ] doi: 10.3969/j.issn.1673-6338.2014.04.017
[9]	丁效. 句子级中文事件抽取关键技术研究[D].哈尔滨:哈尔滨工业大学,2011.
	[ Ding X.Research on sentence level Chinese event extraction[D]. Harbin : Harbin Institute of Technology, 2011. ]
[10]	吴家皋,周凡坤,张雪英. HMM模型和句法分析相结合的事件属性信息抽取[J].南京师大学报(自然科学版),2014,37(1):30-34. doi: 10.3969/j.issn.1001-4616.2014.01.005
	[ Wu J G, Zhou F K, Zhang X Y.Research of the extraction method of event properties based on the combining of HMM and syntactic analysis[J]. Journal of Nanjing Normal University (Natural Science Edition), 2014,37(1):30-34. ] doi: 10.3969/j.issn.1001-4616.2014.01.005
[11]	马林兵,龚健雅.空间信息自然语言查询接口的研究与应用[J].武汉大学学报·信息科学版,2003,28(3):301-305. doi: 10.3321/j.issn:1671-8860.2003.03.009
	[ Ma L B, Gong J Y.Application of spatial information natural language query interface[J]. Geomatics and Information Science of Wuhan University, 2003,28(3):301-305.] doi: 10.3321/j.issn:1671-8860.2003.03.009
[12]	乐小虬,杨崇俊,于文洋.基于空间语义角色的自然语言空间概念提取[J].武汉大学学报(信息科学版),2005,30(12):1011-3011. doi: 10.3321/j.issn:1671-8860.2005.12.017
	[ Le X Q, Yang C J, Yu W Y.Spatial concept extraction based on spatial semantic role in natural language[J]. Geomatics and Information Science of Wuhan University, 2005,30(12):1011-3011. ] doi: 10.3321/j.issn:1671-8860.2005.12.017
[13]	乐小虬,杨崇俊.非受限文本中深层空间语义的识别方法[J].计算机工程,2006,32(4):36-38. doi: 10.3969/j.issn.1000-3428.2006.04.013
	[ Le X Q, Yang C J.Recognition of deep spatial semantics from unrestricted text[J]. Computer Engineering, 2006,32(4):36-38. ] doi: 10.3969/j.issn.1000-3428.2006.04.013
[14]	蒋文明. 面向中文文本的空间方位关系抽取方法研究[D].南京:南京师范大学,2010.
	[ Jiang W M.Automatic Extraction of Spatial Relations in Chinese text[D]. Nanjing: Nanjing Normal University, 2010. ]
[15]	Li R, Tao X, Tang L, et al.Using maximum entropy model for Chinese text categorization[C]. Hangzhou: Asia-Pacific Web Conference, 2004:578-587.
[16]	李荣陆,王建会,陈晓云,等.使用最大熵模型进行中文文本分类[J].计算机研究与发展, 2005,42(1):94-101. doi: 10.1007/978-3-540-24655-8_63
	[ Li R L, Wang J H, Chen X Y, et al.Using maximum entropy model for Chinese text catagorization[J]. Journal of Computer Research and Development, 2005,42(1):94-101. ] doi: 10.1007/978-3-540-24655-8_63
[17]	肖雪. 基于最大熵模型的中文文本层次分类方法[J].计算机与网络,2015(9):36-38. doi: 10.3969/j.issn.1008-1739.2015.09.031
	[ Xiao X.Hierarchical text categorization methods based on maximum entropy model[J]. Computer & Network, 2015(9):36-38. ] doi: 10.3969/j.issn.1008-1739.2015.09.031
[18]	王江伟. 基于最大熵模型的中文命名实体识别[D].南京:南京理工大学,2005.
	[ Wang J W.Research on Chinese named entity recognition based on maximum entropy model[D]. Nanjing: Nanjing University of Science and Technology, 2005. ]
[19]	王胜,朱明.基于最大熵马尔可夫模型的地址信息抽取[J].计算机工程与应用,2005,41(21):192-194. doi: 10.3321/j.issn:1002-8331.2005.21.057
	[ Wang S, Zhu M.Address information extraction based on MEMM[J]. Computer Engineering and Applications, 2005,41(21):192-194. ] doi: 10.3321/j.issn:1002-8331.2005.21.057
[20]	钱晶,张玥杰,张涛.基于最大熵的汉语人名地名识别方法研究[J].小型微型计算机系统,2006,27(9):1761-1765. doi: 10.3969/j.issn.1000-1220.2006.09.038
	[ Qian J, Zhang Y J, Zhang T.Research on Chinese person name and location name recognition based on maximum entropy model[J]. Mini-Micro Systems, 2006,27(9):1761-1765. ] doi: 10.3969/j.issn.1000-1220.2006.09.038
[21]	Kambhatla N.Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations[C]. Barcelona: Association for Computational Linguistics, 2014:22.
[22]	Kumar M A, Gopal M.A comparison study on multiple binary-class SVM methods for unilabel text categorization[J]. Pattern Recognition Letters, 2010,31(11):1437-1444. doi: 10.1016/j.patrec.2010.02.015
[23]	冯永,李华,钟将,等.基于自适应中文分词和近似SVM的文本分类算法[J].计算机科学, 2010,37(1):251-254. doi: 10.3969/j.issn.1002-137X.2010.01.061
	[Feng Y, Li H, Zhong J, et al. Text classification algorithm based on adaptive Chinese word segmentation and proximal SVM[J]. Computer Science, 2010,37(1):251-254. ] doi: 10.3969/j.issn.1002-137X.2010.01.061
[24]	王金华,喻辉,产文,等.基于KNN+层次SVM的文本自动分类技术[J].计算机应用与软件,2016,33(2):38-41. doi: 10.3969/j.issn.1000-386x.2016.02.009
	[ Wang J H, Yu H, Chan W, et al.Integrating KNN and Hierarchical SVM for Automatic Text Classification[J]. Computer Applications and Software, 2016,33(2):38-41. ] doi: 10.3969/j.issn.1000-386x.2016.02.009
[25]	李丽双,黄德根,陈春荣,等.用支持向量机进行中文地名识别的研究[J].小型微型计算机系统,2005,26(8):1416-1419. doi: 10.3969/j.issn.1000-1220.2005.08.029
	[ Li L S, Huang D G, Chen C R, et al.Research on method of automatic recognition of Chinese place names based on support vector machines[J]. Mini-Micro Systems, 2005,26(8):1416-1419. ] doi: 10.3969/j.issn.1000-1220.2005.08.029
[26]	李丽双,黄德根,陈春荣,等.SVM与规则相结合的中文地名自动识别[J].中文信息学报,2006,20(5):51-57. doi: 10.3969/j.issn.1003-0077.2006.05.008
	[ Li L S, Huang D G, Chen C R, et al.Identifying Chinese place names based on support vector machines and rules[J]. Journal of Chinese Information Processing, 2006,20:51-57. ] doi: 10.3969/j.issn.1003-0077.2006.05.008
[27]	唐晋韬,王挺,周会平.面向中文文本的时间本体构建和自动扩充[C].北京:全国信息检索与内容安全学术会议, 2005.
	[ Tang J T, Wnag T, Zhou H P.Time ontology construction and auto-population towards Chinese text[C]. Beijing: NCIRCS, 2005. ]
[28]	周凡坤. 面向领域的文本信息抽取方法研究[D].南京:南京邮电大学,2014.
	[ Zhou F K.Research of domain-oriented extraction method of text information[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2014. ]
[29]	Wang T, Li Y, Bontcheva K, et al.Automatic extraction of hierarchical relations from text[M]. Budva: Springer Berlin Heidelberg, 2006.
[30]	Jiang J, Zhai C X.A systematic exploration of the feature space for relation extraction[C]. Rochester: Proceedings of NAACL HLT 2007, 2007:113-120.
[31]	Bunescu R C, Mooney R J.Subsequence kernels for relation extraction[C]. International Conference on Neural Information Processing Systems, 2005:171-178.
[32]	Zhou G D, Zhang M, Ji D H, et al.Tree kernel-based relation extraction with context-sensitive structured parse tree information[C]. Prague: 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2007.
[33]	项乐安. 基于多标签分类的空间关系抽取研究[D].南京:南京师范大学,2013.
	[ Xiang L A.Spatial relation extraction based on multi-label classification[D]. Nanjing: Nanjing Normal University, 2013. ]
[34]	张春元. 基于CRFs的新闻网页主题内容自动抽取方法[J].广西师范大学学报(自然科学版),2011,29(1):138-142. doi: 10.3969/j.issn.1001-6600.2011.01.028
	[ Zhang C Y.Automatic web news content extraction based on CRFs[J]. Journal of Guangxi Normal University:Natural Science Edition, 2011,29(1):138-142. ] doi: 10.3969/j.issn.1001-6600.2011.01.028
[35]	梁吉光,田俊华,姜杰.基于改进HMM的文本信息抽取模型[J].计算机工程,2011,37(20):178-179. doi: 10.3969/j.issn.1000-3428.2011.20.061
	[ Liang J G, Tian J H, Jiang J.Text information extraction model based on improved HMM[J]. Computer Engineering, 2011,37(20):178-179. ] doi: 10.3969/j.issn.1000-3428.2011.20.061
[36]	史庆伟,郭朋亮.基于LDA的条件随机场主题模型研究[J].计算机工程与应用,2015,51(7):131-135. doi: 10.3778/j.issn.1002-8331.1305-0240
	[ Shi Q W, Guo P L.Conditional random fields topic model based on LDA model[J]. Computer Engineering and Applications, 2015,51(7):131-135. ] doi: 10.3778/j.issn.1002-8331.1305-0240
[37]	马龙. 基于条件随机域模型的中文地名识别的研究[D].大连:大连理工大学,2009.
	[ Ma L.A study on chinese location names recognition based on conditional random fields[D]. Dalian: Dalian University of Technology, 2009. ]
[38]	高国洋,戚银城,潘德锋.基于条件随机场与规则相结合的中文地名识别[J].电脑开发与应用,2009,22(8):26-28. doi: 10.3969/j.issn.1003-5850.2009.08.005
	[ Gao G Y, Qi Y C, Pan D F.Recognition of Chinese location name based on combination of conditional random fields with Multi-rules[J]. Computer Development & Applications, 2009,22(8):26-28. ] doi: 10.3969/j.issn.1003-5850.2009.08.005
[39]	邬伦,刘磊,李浩然,等.基于条件随机场的中文地名识别方法[J].武汉大学学报·信息科学版,2017,42(2):150-156. doi: 10.13203/j.whugis20141009
	[ Wu L, Liu L, Li H R, et al.A Chinese toponym recognition method based on conditional random field[J]. Geomatics and Information Science of Wuhan University, 2017,42(2):150-156. ] doi: 10.13203/j.whugis20141009
[40]	Scheffer T, Decomain C, Wrobel S.Active hidden markov models for information extraction[C]. Cascais: International Conference on Advances in Intelligent Data Analysis, 2001:309-318
[41]	Ojokoh B, Zhang M, Tang J.A trigram hidden Markov model for metadata extraction from heterogeneous references[J]. Information Sciences, 2011,181(9):1538-1551. doi: 10.1016/j.ins.2011.01.014
[42]	Zhou D, He Y.Biomedical events extraction using the hidden vector state model[J]. Artificial Intelligence in Medicine, 2011,53(3):205-213. doi: 10.1016/j.artmed.2011.08.002 pmid: 21945347
[43]	董静,孙乐,冯元勇,等.中文实体关系抽取中的特征选择研究[J].中文信息学报,2007,21(4):80-85,91. doi: 10.3969/j.issn.1003-0077.2007.04.012
	[ Dong J, Sun L, Feng Y Y, et al.Chinese automatic entity relation extraction[J]. Journal of Chinese Information Processing, 2007,21(4):80-85,91. ] doi: 10.3969/j.issn.1003-0077.2007.04.012
[44]	张春菊. 中文文本中事件时空与属性信息解析方法研究[D].南京:南京师范大学,2013.
	[ Zhang C J.Interpretation of event spatio-temporal and attribute information in Chinese Text[D]. Nanjing: Nanjing Normal University, 2013. ]
[45]	Sankaranarayanan J, Samet H, Teitler B E, et al.TwitterStand:news in tweets[C]. ACM Sigspatial International Conference on Advances in Geographic Information Systems, 2009:42-51.
[46]	路金泉,徐开勇,戴乐育.基于文本过滤的贝叶斯分类算法的改进[J].计算机与现代化,2016(9):100-103. doi: 10.3969/j.issn.1006-2475.2016.09.022
	[ Lu J Q, Xu K Y, Dai L Y.Improvement of bayes classification algorithm based on text filtering[J]. Computer and Modernization, 2016(9):100-103. ] doi: 10.3969/j.issn.1006-2475.2016.09.022
[47]	武建军,李昌兵.基于互信息的加权朴素贝叶斯文本分类算法[J].计算机系统应用,2017,26(7):178-182. doi: 10.15888/j.cnki.csa.005840
	[ Wu J J, Li C B.Mutual information-based weighted naive bayes text classification algorithm[J]. Computer Systems & Applications, 2017,26(7):178-182. ] doi: 10.15888/j.cnki.csa.005840
[48]	刘杰. 基于动态贝叶斯网的中文专有名词识别[D].太原:山西大学,2006.
	[ Liu J.Chinese proper names recognition based on dynamic bayesian network[D]. Taiyuan: Shanxi University, 2006. ]
[49]	顾雪峰. 基于动态粒度思想的实体关系识别方法研究[D].太原:山西大学,2006.
	[ Gu X F.Research on entity relation recognition based on dynamic granulation theory[D]. Taiyuan: Shanxi University, 2006. ]
[50]	杨俊,陈贤富.基于KPCA和RBF网络的文本分类研究[J].微电子学与计算机,2010,27(3):122-125.
	[ Yang J, Chen X F.Text categorization based on KPCA and RBF neural network[J]. Microelectronics & Computer, 2010,27(3):122-125. ]
[51]	吕淑宝,王明月,翟祥,等.一种深度学习的信息文本分类算法[J].哈尔滨理工大学学报,2017,22(2):105-111. doi: 10.15938/j.jhust.2017.02.020
	[ Lu S B, Wang M Y, Zhai X, et al.An information text classification algorithm based on DBN[J]. Journal of Harbin University of Science and Technology, 2017,22(2):105-111. ] doi: 10.15938/j.jhust.2017.02.020
[52]	郭东亮,刘小明,郑秋生.基于卷积神经网络的互联网短文本分类方法[J].计算机与现代化,2017(4):78-81. doi: 10.3969/j.issn.1006-2475.2017.04.016
	[ Guo D L, Liu X M, Zheng Q S.Internet short-text classification method based on CNNs[J]. Computer and Modernization, 2017(4):78-81. ] doi: 10.3969/j.issn.1006-2475.2017.04.016
[53]	欧嘉致,陈凯江.基于NN/HMM混合模型的汉语地名识别系统[J].计算机工程与应用,2002,38(23):220-222. doi: 10.3321/j.issn:1002-8331.2002.23.074
	[ Ou J Z, Chen K J, Li Z G.Hybrid neural-network/HMM Based mandarin place name recognition system[J]. Computer Engineering and Applications, 2002,38(23):220-222. ] doi: 10.3321/j.issn:1002-8331.2002.23.074
[54]	李帅,黄玺瑛,董家瑞.一种基于神经网络的特定文本信息提取方法[C].郑州:中国科协年会,2008.
	[ Li S, Huang X Y, Dong J R.An extracting measure of the specific text information based on neural-network[C]. Zhengzhou: The annual meeting of China Association for Science and Technology, 2008. ]
[55]	吕国英,冯艳,李茹.基于中文框架语义的信息抽取研究[C].北京:全国信息检索与内容安全学术会议,2008.
	[ Lv G Y, Feng Y, Li R.Research of information extraction based on Chinese FrameNet[C]. Beijing: NCIRCS, 2008. ]
[56]	叶开. 基于词向量的在线评论话题及其特征抽取研究[D].成都:电子科技大学,2016.
	[ Ye K.Topic and feature extraction in online reviews based on Word2Vec[D]. Chengdu: University of Electronic Science and Technology of China, 2016. ]
[57]	Jiang S, Pang G, Wu M, et al.An improved K-nearest-neighbor algorithm for text categorization[J]. Expert Systems with Applications, 2012,39(1):1503-1509. doi: 10.1016/j.eswa.2011.08.040
[58]	周庆平,谭长庚,王宏君,等.基于聚类改进的KNN文本分类算法[J].计算机应用研究, 2016,33(11):3374-3377. doi: 10.3969/j.issn.1001--3695.2016.11.038
	[Zhou Q P, Tan C G, Wang H J, et al. Improved KNN text classification algorithm based on clustering[J]. Application Research of Computers, 2016,33(11):3374-3377. ] doi: 10.3969/j.issn.1001--3695.2016.11.038
[59]	戚后林,顾磊.概率潜在语义分析的KNN文本分类算法[J].计算机技术与发展,2017,27(7):1-5. doi: 10.3969/j.issn.1673-629X.2017.07.013
	[ Qi H L, Gu L.KNN text classification algorithm with probabilistic latent Semantic Analysis[J]. Computer Technology and Development, 2017,27(7):1-5. ] doi: 10.3969/j.issn.1673-629X.2017.07.013
[60]	高昂,程越,李进,等.网络新闻事件分类体系及事件本体建模语料库标准化研究[J].情报工程,2017,3(5):43-52. doi: 10.3772/j.issn.2095-915x.2017.05.006
	[ Gao A, Cheng Y, Li J, et al.The standardization study of netnews events classification system and the events ontology modeling corpus[J]. Discovery and Research, 2017,3(5):43-52.] doi: 10.3772/j.issn.2095-915x.2017.05.006
[61]	张春菊,张雪英,王曙,等.中文文本的事件时空信息标注[J].中文信息学报,2016,30(3):213-222.
	[ Zhang C J, Zhang X Y, Wang S, et al.Annotation of Spatio-Temporal Information of Event in Chinese Text[J]. Journal of Chinese Information Processing, 2016,30(3):213-222. ]

	主题分类及提取	时间与地名提取	属性信息抽取	空间关系提取
基于规则的方法	Rafea等^[5]、Petkos等^[6]	谭红叶等^[7]、肖计划^[8]	丁效^[9]、吴家皋等^[10]	马林兵等^[11]、乐小虬等^[12]、乐小虬等^[13]、蒋文明^[14]
最大熵模型	Li等^[15]、李荣陆等^[15]、肖雪^[17]	王江伟^[18]、王胜等^[19]、钱晶等^[20]	-	Kambhatla等^[21]
支持向量机	Kumar等^[22]、冯永等^[23]、王金华等^[24]	李丽双等^[25]、李丽双等^[26]、唐晋韬^[27]	周凡坤^[28]	Wang等^[29]、Jiang等^[30]、Bunescu等^[31]、ZHOU等^[32]、项乐安^[33]
马尔科夫模型	张春元^[34]、梁吉光等^[35]、史庆伟等^[36]	马龙^[37]、高国洋等^[38]、邬伦等^[39]	Scheffer等^[40]、Ojokoh等^[41]、梁吉光等^[35]、Zhou等^[42]、吴家皋等^[10]	董静等^[43]、张春菊^[44]
贝叶斯分类	Sankaranarayanan等^[45],路金泉等^[46]、武建军等^[47]、	刘杰^[48]	-	顾雪峰^[49]
神经网络	杨俊等^[50]、吕淑宝等^[51]、郭东亮等^[52]	欧嘉致等^[53]	李帅等^[54]、吕国英等^[55]、叶开^[56]	-
最邻近方法KNN	Jiang等^[57]、周庆平等^[58]、戚后林等^[59]	-	-	-

代码	名称	代码	名称	代码	名称	代码	名称
0100	自然灾害	0200	事故灾难	0300	公共卫生事件	0400	社会安全事件
0101	水旱灾害	0201	战争和暴力	0301	传染病疫情	0401	恐怖袭击事件
0102	气象灾害	0202	工矿商贸安全事故	0302	群体性不明原因疾病	0402	重大刑事案件
0103	地震灾害	0203	交通运输安全事故	0303	食品安全和职业危害	0403	经济安全事件
0104	地质灾害	0204	城市生命线事故	0304	动物疫情	0404	涉外突发事件
0105	海洋灾害	0205	通讯安全事故	0399	其他	0405	规模较大的群体性事件
0106	生物灾害	0206	环境污染和生态破坏			0406	民族宗教
0107	森林草原火灾	0207	严重火灾			0407	反政府和反社会主义骚乱暴动
0108	宇宙灾害	0208	中毒事件			0499	其他
0199	其他	0209	急性化学事故
		0210	放射事故
		0211	医药事故
		0212	探险遇难
		0213	旅游事故
		0299	其他

要素类型名称		地震事件要素	数据类型
类型		事件类型	字符型
对象		地震名称	字符型
编号		记录编号	整数型
时间		发震时间	年-月-日-时-分-秒（时间型）
空间		发震地点	字符型
		震中纬度	实数型
		震中经度	实数型
		地震深度	实数型
属性	原因	发震原因	字符型
	结果	地震震级	实数型
		地震烈度	整型
		死亡人数	整型
		受伤人数	整型
		失踪人数	整型
		受灾人数	整型
		安置人数	整型
		建筑损坏	整型
		经济损失	实数型
		受灾范围	实数型
		记录时间	年-月-日-时-分-秒（时间型）
	行为	震感程度	字符型
	行为	天气状况	字符型
		救援方式	捐款/实地/安置/重建/
	影响	救援人数	整型
		救援资金	实数型
来源	发布时间	发布时间	年-月-日-时-分-秒（时间型）
	网址	信息网址	字符型
	可靠性	信息可靠性级别	国家媒体/地方媒体/企业媒体

属性列	记录1	记录2	记录3	……
事件类型	地震	地震	地震	……
地震名称	邵通鲁甸地震	邵通鲁甸地震	邵通鲁甸地震
记录编号	36 786	64 873	4783
发震时间	2014-08-03-00-00-00	2014-08-03-16-30-00	2014-08-03-00-00-00
发震地点	云南省昭通市鲁甸县	云南省昭通市鲁甸县	云南省昭通市鲁甸县
震中经度	-	103.3	103.725
震中纬度	-	27.1	27.34
震源深度	-	12 000	12 000
发震原因	-	-	-
地震震级	6.5	6.5	6.5
地震烈度	-	-	-
死亡人数	367	589	617
受伤人数	1801	2401	3143
失踪人数	5	9	112
受灾人数	-	1 088 400	1 088 400
安置人数	-	229 700	229 700
建筑损坏	-	80 900	-
经济损失	-	-	-
受灾范围	-	-	-
记录时间	2014-08-03	2014-08-06-10-30-00	2014-08-21-00-00-00
震感程度	-	-	-
天气状况	-	-	-
救援方式	捐款	安置	安置
救援人数	-	-	-
救援资金	-	-	-
发布时间	2014-08-05-09-57-00	2014-08-06-12-58-08	2014-08-21-22-47-42
信息网址	http://dl.sina.com.cn/news/wenti/2014-08-05/095732477.html	http://news.iqilu.com/china/gedi/2014/0806/2093139.shtml	http://news.china.com.cn/rollnews/education/live/2014-08/21/content_28320532.htm
可靠性	企业媒体	地方媒体	国家媒体

Public Security Event Themed Web Text Structuring

RichHTML

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 10

References 61

Related Articles 1

Metrics

Comments

Recommended 0