This paper describes how to use the amount of information in a sentence interpretation as a measure of interpreting input sentences in a natural language understanding system. In this paper, an interpretation of a sentence is considered to be a proposition, and the amount of information of the interpretation is defined according to a listener's model with a knowledge base composed of a literal set and a logical implication set, both of which are defined within the framework of propositional logic. When a given sentence can be analyzed syntactically and semantically into more than one interpretation, the most informative interpretation is selected. The theory of selecting the most informative interpretation by the proposed measure is reasonable in the sense that communication is an act whereby messages are passed on with the least possible effort. The presented theory for disambiguation is applied to a practical procedure for anaphoric ambiguity resolution, as an example of the disambiguation problem, which forms part of a question-answering system. Furthermore, a conversation experiment was carried out, and it was found that ninety-three percent of referents corresponding to anaphoric expressions could be correctly chosen.