The paper describes the way in which a Preference Semantics system for natural language analysis and generation tackles a difficult class of anaphoric inference problems: those requiring either analytic (conceptual) knowledge of a complex sort, or requiring weak inductive knowledge of the course of events in the real world. The method employed converts all available knowledge to a canonical template form and endeavors to create chains of non-deductive inferences from the unknowns to the possible referents. Its method for this is consistent with the overall principle of ‘‘semantic preference’’ used to set up the original meaning representation
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Wilks, Y. (2007). A Preferential, Pattern-Seeking, Semantics for Natural Language Inference. In: Ahmad, K., Brewster, C., Stevenson, M. (eds) Words and Intelligence I. Text, Speech and Language Technology, vol 35. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5285-5_5
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DOI: https://doi.org/10.1007/1-4020-5285-5_5
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