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Certainty Identification in Texts: Categorization Model and Manual Tagging Results

  • Victoria L. Rubin
  • Elizabeth D. Liddy
  • Noriko Kando
Part of the The Information Retrieval Series book series (INRE, volume 20)

Abstract

This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented as a type of subjective information available in texts. Statements with explicit certainty markers were identified and categorized according to four hypothesized dimensions — level, perspective, focus, and time of certainty. The preliminary results reveal an overall promising picture of the presence of certainty information in texts, and establish its susceptibility to manual identification within the proposed four-dimensional certainty categorization analytical framework. Our findings are that the editorial sample group had a significantly higher frequency of markers per sentence than did the sample group of the news stories. For editorials, high level of certainty, writer’s point of view, and future and present time were the most populated categories. For news stories, the most common categories were high and moderate levels, directly involved third party’s point of view, and past time. These patterns have positive practical implications for automation.

Keywords

Subjectivity manual tagging natural language processing uncertainty epistemic comments evidentials hedges certainty expressions point of view annotating opinions 

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Copyright information

© Springer 2006

Authors and Affiliations

  • Victoria L. Rubin
    • 1
  • Elizabeth D. Liddy
    • 1
  • Noriko Kando
    • 2
  1. 1.School of Information Studies Center for Natural Language ProcessingSyracuse UniversitySyracuseUSA
  2. 2.National Institute of InformaticsTokyoJapan

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