事件框架的应急地理信息抽取
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摘要
针对现有的通用信息抽取技术无法实现结构化的应急信息抽取和完整的地名地址识别的问题,该文提出了面向众源数据的地理信息抽取技术,采用了基于事件框架的应急信息抽取方法,利用层次模型与N-最短路径算法,解决了完整的地名地址的识别与提取的问题,较好地实现了应急地理信息的自动化抽取。通过实验的进一步分析,此方法取得了最优化的应急地理信息抽取结果。实验表明,基于事件框架的应急地理信息抽取技术不仅可以快速有效地抽取应急地理信息,还能够识别完整的地名地址。
In view of the problem that the common information extraction method cannot extract the structured emergency event information and recognize addresses accurately;the general information abstract tool cannot completely identify the emergency geographic information;these ways also do not have an accurate assessment of these results of extraction.So,this paper proposed an emergency information extraction technology based on event framework.This technique was to solve the problem of emergency information extraction.It mainly used a hierarchical model and the shortest path algorithm and allowed the toponomy pieces to be joined as a full address.The event framework was better to achieve the emergency geographic information extraction automatically.Through the further analysis of the experiment,this method obtained optimal emergency geographic information extraction results.Experiments showed that event frame technology could not only effectively extract the sources of emergency geographic information,but also identify the full name of the address.
In view of the problem that the common information extraction method cannot extract the structured emergency event information and recognize addresses accurately;the general information abstract tool cannot completely identify the emergency geographic information;these ways also do not have an accurate assessment of these results of extraction.So,this paper proposed an emergency information extraction technology based on event framework.This technique was to solve the problem of emergency information extraction.It mainly used a hierarchical model and the shortest path algorithm and allowed the toponomy pieces to be joined as a full address.The event framework was better to achieve the emergency geographic information extraction automatically.Through the further analysis of the experiment,this method obtained optimal emergency geographic information extraction results.Experiments showed that event frame technology could not only effectively extract the sources of emergency geographic information,but also identify the full name of the address.
引文
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[2]SUN X,ZHOU Y.Modeling emergency event and emergency response decision based on event evolvement[C]//Proceedings of 8th International Conference on Service Systems and Service Management.Chicago:IEEE,2011:1-5.
[3]OZEKI M,SHIMAZAKI K,YI T.Exploring elements of disaster prevention consciousness:based on interviews with anti-disaster professionals[J].Journal of Disaster Research,2017,12(3):631-638.
[4]FIEDRICH U D I F,ZLATANOVA S.Emergency mapping[M]//Encyclopedia of Earth Sciences.[S.l]:[s.n],2013:272-276.
[5]CALDERON A C,JOHNSON P.Information extraction in emergency management missions:an adaptive multiagent approach[J].International Journal of Emergency Management,2017,13(3):216.
[6]EFTIMOV T,SELJAK B K,KOROSEC P.A rulebased named-entity recognition method for knowledge extraction of evidence-based dietary recommendations[J].Plos One,2017,12(6):e0179488.
[7]FAN H,GUO D,LI H.Extraction of spatio-temporal information of earthquake event based on semantic technology[C]//International Symposium on Multispectral Image Processing&Pattern Recognition.[S.l]:[s.n],2015:9815.
[8]WANG S,YUAN Y,PEI T,et al.A framework for event information extraction from Chinese news online[M]//Part of the Advances in Geographic Information Science Book Series.[S.l]:[s.n],2017.
[9]BRAMER M.Data for data mining[M].London:Springer,2007:11-21.
[10]OLSON D L,DELEN D.Data mining process[M].Berlin:Springer,2008:9-35.
[11]DIEDERICHJ.Rule extraction from support vector machines:an introduction[M]//Rule Extraction from Support Vector Machines.Berlin:Springer,2008:106-110.
[12]AYODELE T O.Machine learning overview[M].[S.l]:InTech,2010.
[13]ALPAYDINE.Introduction to machine learning[M].Cambridge:the MIT Press,1988.
[14]JEON H I,KIM Y.Efficient,real-time short address allocations for USN devices using LAA(last address assigned)algorithm[C]//The 9th International Conference on Advanced Communication Technology.Kobe:IEEE,2007:689-693..
[15]LAPATA M,KELLER F.Web-based models for natural language processing[J].ACM,2005,2(1):1-31.
[16]GAROUFIK.Planning-based models of natural language generation[J].Language&Linguistics Compass,2014,8(1):1-10.
[17]TORREY L,SHAVLIK J,WALKER T,et al.Rule extraction for transfer learning[M].Berlin:Springer.2008:67-82.
[18]NUNEZ H,ANGULO C,CATALA A.Rule extraction based on support and prototype vectors[M].Berlin:Springer,2008:109-134.
[19]DAVID H.T.Bayes extraction of component failure information from boolean module test data[J].Communications in Statistics-Theory and Methods,2007,16(10):2873-2883.
[20]KAI M T.Precision and recall[M].New York:Springer,2011.
[21]HAUSSER R.Utilizing grammatical relations to improve recall and precision in a textual database[C]//Sixth International Conference on Natural Computation.Yantai:IEEE,2010:2956-2960.