Interpreting Winograd Schemas Via the SP Theory of Intelligence and Its
  Realisation in the SP Computer Model
  In 'Winograd Schema' (WS) sentences like "The city councilmen refused the
demonstrators a permit because they feared violence" and "The city councilmen
refused the demonstrators a permit because they advocated revolution", it is
easy for adults to understand what "they" refers to but can be difficult for AI
systems. This paper describes how the SP System -- outlined in an appendix --
may solve this kind of problem of interpretation. The central idea is that a
knowledge of discontinuous associations amongst linguistic features, and an
ability to recognise such patterns of associations, provides a robust means of
determining what a pronoun like "they" refers to. For any AI system to solve
this kind of problem, it needs appropriate knowledge of relevant syntax and
semantics which, ideally, it should learn for itself. Although the SP System
has some strengths in unsupervised learning, its capabilities in this area are
not yet good enough to learn the kind of knowledge needed to interpret WS
examples, so it must be supplied with such knowledge at the outset. However,
its existing strengths in unsupervised learning suggest that it has potential
to learn the kind of knowledge needed for the interpretation of WS examples. In
particular, it has potential to learn the kind of discontinuous association of
linguistic features mentioned earlier.
