For solving complex problems in our real world, it is important
not only to get explicit information, but also to identify appropriate
information by selecting it from a huge amount of knowledge stored in
memory. The most important process is to select appropriate knowledge
which is essential to interpretation of current information, and
to ignore inappropriate knowledge which is irrelevant. In Relevance
Theory, it is claimed that an optimal relevance gives the most
appropriate interpretation by means of deductive inference.
This paper provides a computational method and a quantification
of the cognitive relevance based on Relevance Theory
and proposes a system of an interpretation of both counterfactual
conditionals and `because'-type sentences.
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