| Item type |
SIG Technical Reports(1) |
| 公開日 |
2017-12-15 |
| タイトル |
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|
タイトル |
Finding Related Events Based on Bursty Phrase Detection and Clustering |
| タイトル |
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言語 |
en |
|
タイトル |
Finding Related Events Based on Bursty Phrase Detection and Clustering |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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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 |
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Graduate School of Information, Production and Systems, Waseda University |
| 著者所属 |
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Graduate School of Information, Production and Systems, Waseda University |
| 著者所属(英) |
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en |
|
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Graduate School of Information, Production and Systems, Waseda University |
| 著者所属(英) |
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|
en |
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Graduate School of Information, Production and Systems, Waseda University |
| 著者名 |
Linfeng, Qi
Mizuho, Iwaihara
|
| 著者名(英) |
Linfeng, Qi
Mizuho, Iwaihara
|
| 論文抄録 |
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内容記述タイプ |
Other |
|
内容記述 |
Wikipedia is known as the largest up-to-date online encyclopedia, in which articles are versioned and these edits are stored as revisions. In this paper we propose a new method to find related bursty edit events, based on detecting and clustering temporally significant phrases by their bursts over time, from revisions of articles. We discuss evaluation functions to find phrases that are semantically representative as well as temporally significant. After bursts are detected from the time series for each phrase, these phrases are clustered by their temporal similarities, using FastDTW. We evaluate how clustering quality is affected by the time resolution of FastDTW, and discuss optimum time resolution in terms of average burst duration. Experimental results show clustered phrases share similar burst patterns, which can be linked to related real-world events. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Wikipedia is known as the largest up-to-date online encyclopedia, in which articles are versioned and these edits are stored as revisions. In this paper we propose a new method to find related bursty edit events, based on detecting and clustering temporally significant phrases by their bursts over time, from revisions of articles. We discuss evaluation functions to find phrases that are semantically representative as well as temporally significant. After bursts are detected from the time series for each phrase, these phrases are clustered by their temporal similarities, using FastDTW. We evaluate how clustering quality is affected by the time resolution of FastDTW, and discuss optimum time resolution in terms of average burst duration. Experimental results show clustered phrases share similar burst patterns, which can be linked to related real-world events. |
| 書誌レコードID |
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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 2017-DBS-166,
号 13,
p. 1-6,
発行日 2017-12-15
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-871X |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-DBS17166013.pdf (367.8 kB)
