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  • 标题:A Pattern-Mining Driven Study on Differences of Newspapers in expressing Temporal Information
  • 本地全文:下载
  • 作者:Yingxue Fu ; Elaine Uí Dhonnchadha
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2020
  • 卷号:10
  • 期号:14
  • 页码:111-129
  • DOI:10.5121/csit.2020.101409
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:This paper studies the differences between different types of newspapers in expressing temporal information, which is a topic that has not received much attention. Techniques from the fields of temporal processing and pattern mining are employed to investigate this topic. First, a corpus annotated with temporal information is created by the author. Then, sequences of temporal information tags mixed with part-of-speech tags are extracted from the corpus. The TKS algorithm is used to mine skip-gram patterns from the sequences. With these patterns, the signatures of the four newspapers are obtained. In order to make the signatures uniquely characterize the newspapers, we revise the signatures by removing reference patterns. Through examining the number of patterns in the signatures and revised signatures, the proportion of patterns containing temporal information tags and the specific patterns containing temporal information tags, it is found that newspapers differ in ways of expressing temporal information.
  • 关键词:Pattern Mining ;TKS algorithm ;Temporal Annotation ;Tabloids and Broadsheets.
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