A preferential, pattern-seeking, Semantics for natural language inference

摘要

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-reductive 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.