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Annotating the Focus of Negation in terms of Questions Under Discussion

机译:用讨论中的问题注释否定的焦点

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摘要

Blanco & Moldovan (Blanco and Moldovan, 2011) have empirically demonstrated that negated sentences often convey implicit positive inferences, or focus, and that these inferences are both human annotatable and machine learnable. Concentrating on their annotation process, this paper argues that the focus-based implicit positivity should be separated from concepts of scalar implicature and neg-raising, as well as the placement of stress. We show that a model making these distinctions clear and which incorporates the pragmatic notion of question under discussion yields k rates above .80, but that it substantially deflates the rates of focus of negation in text.
机译:Blanco和Moldovan(Blanco和Moldovan,2011年)从经验上证明,否定的句子通常会传达隐含的积极推论或重点,这些推论既是人类可注释的,也是机器可学习的。着重于他们的注释过程,本文认为基于焦点的隐式积极性应与标量隐含和否定概念以及重音的位置分开。我们显示出一个模型,使这些区别清晰可见,并结合了讨论中的务实问题概念,得出的k比率高于.80,但它实质上抵消了文本中的否定焦点比率。

著录项

  • 来源
    《》|2012年|65-69|共5页
  • 会议地点 Jeju Island(KR)
  • 作者

    Pranav Anand; Craig Martell;

  • 作者单位

    Department of Linguistics University of California, Santa Cruz Santa Cruz, CA 95064 USA;

    Department of Computer Science Naval Postgraduate School Monterey, CA USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-26 14:23:30

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