Anticipatory event detection via classification
The idea of event detection is to identify interesting patterns from a constant stream of incoming news documents. Previous research in event detection has largely focused on identifying the first event or tracking subsequent events belonging to a set of pre-assigned topics such as earthquakes, airl...
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sg-smu-ink.sis_research-10202018-06-21T06:38:24Z Anticipatory event detection via classification QI, He CHANG, Kuiyu LIM, Ee Peng The idea of event detection is to identify interesting patterns from a constant stream of incoming news documents. Previous research in event detection has largely focused on identifying the first event or tracking subsequent events belonging to a set of pre-assigned topics such as earthquakes, airline disasters, etc. In this paper, we describe a new problem, called anticipatory event detection (AED), which aims to detect if a user-specified event has transpired. AED can be viewed as a personalized combination of event tracking and new event detection. We propose using sentence-level and document-level classification approaches to solve the AED problem for some restricted domains; given some user preferred topic event transition, we first train the corresponding event transition model, and then detect the occurrence of the transition for the stream of news covering the topic. Our experimental results on both terrorist-related and commercial events demonstrate the feasibility of our proposed AED solutions. 2007-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/21 info:doi/10.1007/s10257-007-0047-z https://ink.library.smu.edu.sg/context/sis_research/article/1020/viewcontent/He2007_Article_AnticipatoryEventDetectionViaC.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing |
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Databases and Information Systems Numerical Analysis and Scientific Computing QI, He CHANG, Kuiyu LIM, Ee Peng Anticipatory event detection via classification |
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The idea of event detection is to identify interesting patterns from a constant stream of incoming news documents. Previous research in event detection has largely focused on identifying the first event or tracking subsequent events belonging to a set of pre-assigned topics such as earthquakes, airline disasters, etc. In this paper, we describe a new problem, called anticipatory event detection (AED), which aims to detect if a user-specified event has transpired. AED can be viewed as a personalized combination of event tracking and new event detection. We propose using sentence-level and document-level classification approaches to solve the AED problem for some restricted domains; given some user preferred topic event transition, we first train the corresponding event transition model, and then detect the occurrence of the transition for the stream of news covering the topic. Our experimental results on both terrorist-related and commercial events demonstrate the feasibility of our proposed AED solutions. |
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QI, He CHANG, Kuiyu LIM, Ee Peng |
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QI, He CHANG, Kuiyu LIM, Ee Peng |
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QI, He |
title |
Anticipatory event detection via classification |
title_short |
Anticipatory event detection via classification |
title_full |
Anticipatory event detection via classification |
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Anticipatory event detection via classification |
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Anticipatory event detection via classification |
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anticipatory event detection via classification |
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Institutional Knowledge at Singapore Management University |
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2007 |
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https://ink.library.smu.edu.sg/sis_research/21 https://ink.library.smu.edu.sg/context/sis_research/article/1020/viewcontent/He2007_Article_AnticipatoryEventDetectionViaC.pdf |
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