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Inferences, suppositions and explanatory extensions in argument interpretation

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Abstract

We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user’s argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation—a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system.

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Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton, VIC, 3800, Australia

    Sarah George, Ingrid Zukerman & Michael Niemann

  2. CVS Dude, 80 Jephson St., Toowong, QLD, 4066, Australia

    Sarah George

Authors
  1. Sarah George
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  2. Ingrid Zukerman
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  3. Michael Niemann
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Correspondence to Ingrid Zukerman.

Additional information

This article integrates and extends research described in George et al., 2004; Zukerman et al., 2004; Zukerman and George, 2005; George et al., 2005. The research described in this article was conducted while Sarah George was employed at Monash University and was supported in part by the ARC Centre for Perceptive and Intelligent Machines in Complex Environments.

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George, S., Zukerman, I. & Niemann, M. Inferences, suppositions and explanatory extensions in argument interpretation. User Model User-Adap Inter 17, 439–474 (2007). https://doi.org/10.1007/s11257-007-9034-9

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  • DOI: https://doi.org/10.1007/s11257-007-9034-9

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