@techreport{oai:ipsj.ixsq.nii.ac.jp:00201553,
 author = {Yingyi, Zhou and Mizuho, Iwaihara and Yingyi, Zhou and Mizuho, Iwaihara},
 issue = {8},
 month = {Dec},
 note = {Wikipedia edit history stores a large number of revisions of each article. Once a big event like a gun shooting happens, articles named after events will be created and edited. As the event evolves, there will be edit bursts and key phrases that are covered in these edit bursts will change over time, which we regard as topic evolutionary patterns. To reveal the evolution of topics, we propose a temporal clustering method based on bursts of phrases in articles. In this paper, we try to simplify the time series by burst detection and component decomposition of temporal sequence. Then in clustering, component pattern vectors of phrases which show high cosine similarity will be clustered and key phrases that can explain burst component patterns in a cluster are selected to indicate the topic of the cluster. We solve the problem by temporal information, instead of traditional semantic information. In experiments, we selected the revisions of two article categories. The results of experiments show that the proposed method can identify evolutionary topic temporal patterns reasonably and effectively., Wikipedia edit history stores a large number of revisions of each article. Once a big event like a gun shooting happens, articles named after events will be created and edited. As the event evolves, there will be edit bursts and key phrases that are covered in these edit bursts will change over time, which we regard as topic evolutionary patterns. To reveal the evolution of topics, we propose a temporal clustering method based on bursts of phrases in articles. In this paper, we try to simplify the time series by burst detection and component decomposition of temporal sequence. Then in clustering, component pattern vectors of phrases which show high cosine similarity will be clustered and key phrases that can explain burst component patterns in a cluster are selected to indicate the topic of the cluster. We solve the problem by temporal information, instead of traditional semantic information. In experiments, we selected the revisions of two article categories. The results of experiments show that the proposed method can identify evolutionary topic temporal patterns reasonably and effectively.},
 title = {Identifying Topic Evolutionary Patterns Based on Component Decomposition of Temporal Sequence},
 year = {2019}
}