Analyzing Disagreements

نویسندگان

  • Beata Beigman Klebanov
  • Eyal Beigman
  • Daniel Diermeier
چکیده

We address the problem of distinguishing between two sources of disagreement in annotations: genuine subjectivity and slip of attention. The latter is especially likely when the classification task has a default class, as in tasks where annotators need to find instances of the phenomenon of interest, such as in a metaphor detection task discussed here. We apply and extend a data analysis technique proposed by Beigman Klebanov and Shamir (2006) to first distill reliably deliberate (non-chance) annotations and then to estimate the amount of attention slips vs genuine disagreement in the reliably deliberate annotations.

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تاریخ انتشار 2008