Detection of Bursty and Significant Keyphrases from Wikipedia edit history

Zihang Chen, Mizuho Iwaihara

研究成果: Conference contribution

1 被引用数 (Scopus)

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 hidden 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 do 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2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538677896
DOI
出版ステータスPublished - 2019 4 1
イベント2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan
継続期間: 2019 2 272019 3 2

出版物シリーズ

名前2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings

Conference

Conference2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019
CountryJapan
CityKyoto
Period19/2/2719/3/2

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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