Analyzing feature trajectories for event detection
We consider the problem of analyzing word trajectories in both time and frequency domains, with the specific goal of identifying important and less-reported, periodic and aperiodic words. A set of words with identical trends can be grouped together to reconstruct an event in a completely un-supervis...
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sg-smu-ink.sis_research-22682018-06-22T02:21:12Z Analyzing feature trajectories for event detection HE, Qi CHANG, Kuiyu LIM, Ee Peng We consider the problem of analyzing word trajectories in both time and frequency domains, with the specific goal of identifying important and less-reported, periodic and aperiodic words. A set of words with identical trends can be grouped together to reconstruct an event in a completely un-supervised manner. The document frequency of each word across time is treated like a time series, where each element is the document frequency - inverse document frequency (DFIDF) score at one time point. In this paper, we 1) first applied spectral analysis to categorize features for different event characteristics: important and less-reported, periodic and aperiodic; 2) modeled aperiodic features with Gaussian density and periodic features with Gaussian mixture densities, and subsequently detected each feature's burst by the truncated Gaussian approach; 3) proposed an unsupervised greedy event detection algorithm to detect both aperiodic and periodic events. All of the above methods can be applied to time series data in general. We extensively evaluated our methods on the 1-year Reuters News Corpus [3] and showed that they were able to uncover meaningful aperiodic and periodic events. 2007-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1269 info:doi/10.1145/1277741.1277779 https://ink.library.smu.edu.sg/context/sis_research/article/2268/viewcontent/p207_he.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 HE, Qi CHANG, Kuiyu LIM, Ee Peng Analyzing feature trajectories for event detection |
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We consider the problem of analyzing word trajectories in both time and frequency domains, with the specific goal of identifying important and less-reported, periodic and aperiodic words. A set of words with identical trends can be grouped together to reconstruct an event in a completely un-supervised manner. The document frequency of each word across time is treated like a time series, where each element is the document frequency - inverse document frequency (DFIDF) score at one time point. In this paper, we 1) first applied spectral analysis to categorize features for different event characteristics: important and less-reported, periodic and aperiodic; 2) modeled aperiodic features with Gaussian density and periodic features with Gaussian mixture densities, and subsequently detected each feature's burst by the truncated Gaussian approach; 3) proposed an unsupervised greedy event detection algorithm to detect both aperiodic and periodic events. All of the above methods can be applied to time series data in general. We extensively evaluated our methods on the 1-year Reuters News Corpus [3] and showed that they were able to uncover meaningful aperiodic and periodic events. |
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HE, Qi CHANG, Kuiyu LIM, Ee Peng |
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HE, Qi CHANG, Kuiyu LIM, Ee Peng |
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HE, Qi |
title |
Analyzing feature trajectories for event detection |
title_short |
Analyzing feature trajectories for event detection |
title_full |
Analyzing feature trajectories for event detection |
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Analyzing feature trajectories for event detection |
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Analyzing feature trajectories for event detection |
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analyzing feature trajectories for event detection |
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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/1269 https://ink.library.smu.edu.sg/context/sis_research/article/2268/viewcontent/p207_he.pdf |
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