Detection of Editing Bursts and Extraction of Significant Keyphrases from Wikipedia Edit History

Zihang Chen, Mizuho Iwaihara

研究成果: Conference contribution

抄録

In an online collaboration system such as Wikipedia, edit history is stored as revisions. Topics of articles or categories grow and fade over time, and evolutionary information is retained in its edit history. We consider that a great amount of information that is related to real life events is embedded in such edit history of documents. This paper focuses on a particular temporal text mining task: effectively extracting keyphrases from burst periods in the edit history of Wikipedia articles or category. We first combine the ARIMA model with a decay function to find typical edit burst periods, then perform keyphrase extraction on burst periods to reveal topics of bursts. However, keyphrase extraction methods, such as TextRank, do not consider temporal trends in text stream. In this paper, we propose TextRank_nfidf which reflects temporal trends into phrase node weights, by computing smoothed difference of editing frequency between revisions. We confirm that detected bursts and keyphrases are matching well with events along the timeline.

本文言語English
ホスト出版物のタイトルBig Data Analyses, Services, and Smart Data, BigDAS 2018
編集者Wookey Lee, Carson K. Leung, Aziz Nasridinov
出版社Springer Science and Business Media Deutschland GmbH
ページ45-65
ページ数21
ISBN(印刷版)9789811587306
DOI
出版ステータスPublished - 2021
イベント6th International Conference on Big Data Applications and Services, BigDAS 2018 - Zhengzhou, China
継続期間: 2018 8 192018 8 22

出版物シリーズ

名前Advances in Intelligent Systems and Computing
899 AISC
ISSN(印刷版)2194-5357
ISSN(電子版)2194-5365

Conference

Conference6th International Conference on Big Data Applications and Services, BigDAS 2018
国/地域China
CityZhengzhou
Period18/8/1918/8/22

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンス(全般)

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