ログイン 新規登録
言語:

WEKO3

  • トップ
  • ランキング


インデックスリンク

インデックスツリー

  • RootNode

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

  1. 研究報告
  2. データベースシステム(DBS)※2025年度よりデータベースとデータサイエンス(DBS)研究会に名称変更
  3. 2019
  4. 2019-DBS-170

Identifying Topic Evolutionary Patterns Based on Component Decomposition of Temporal Sequence

https://ipsj.ixsq.nii.ac.jp/records/201553
https://ipsj.ixsq.nii.ac.jp/records/201553
59f23208-2134-425d-ad15-c78a42052dc3
名前 / ファイル ライセンス アクション
IPSJ-DBS19170008.pdf IPSJ-DBS19170008.pdf (1.1 MB)
Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG.
DBS:会員:¥0, DLIB:会員:¥0
Item type SIG Technical Reports(1)
公開日 2019-12-16
タイトル
タイトル Identifying Topic Evolutionary Patterns Based on Component Decomposition of Temporal Sequence
タイトル
言語 en
タイトル Identifying Topic Evolutionary Patterns Based on Component Decomposition of Temporal Sequence
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_18gh
資源タイプ technical report
著者所属
Graduate School of Information, Production and Systems ,Waseda University
著者所属
Graduate School of Information, Production and Systems ,Waseda University
著者所属
Graduate School of Information, Production and Systems ,Waseda University
著者所属
Graduate School of Information, Production and Systems ,Waseda University
著者所属(英)
en
Graduate School of Information, Production and Systems ,Waseda University
著者所属(英)
en
Graduate School of Information, Production and Systems ,Waseda University
著者所属(英)
en
Graduate School of Information, Production and Systems ,Waseda University
著者所属(英)
en
Graduate School of Information, Production and Systems ,Waseda University
著者名 Yingyi, Zhou

× Yingyi, Zhou

Yingyi, Zhou

Search repository
Mizuho, Iwaihara

× Mizuho, Iwaihara

Mizuho, Iwaihara

Search repository
著者名(英) Yingyi, Zhou

× Yingyi, Zhou

en Yingyi, Zhou

Search repository
Mizuho, Iwaihara

× Mizuho, Iwaihara

en Mizuho, Iwaihara

Search repository
論文抄録
内容記述タイプ Other
内容記述 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.
論文抄録(英)
内容記述タイプ Other
内容記述 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.
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AN10112482
書誌情報 研究報告データベースシステム(DBS)

巻 2019-DBS-170, 号 8, p. 1-6, 発行日 2019-12-16
ISSN
収録物識別子タイプ ISSN
収録物識別子 2188-871X
Notice
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
出版者
言語 ja
出版者 情報処理学会
戻る
0
views
See details
Views

Versions

Ver.1 2025-01-19 20:58:57.921257
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

Yingyi, Zhou, Mizuho, Iwaihara, 2019: 情報処理学会, 1–6 p.

Loading...

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3