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.

Resolving ambiguities in interpreting the user's utterances is one of the most fundamental problems in the development of a question-answering system. The process of disambiguating interpretations requires knowledge and inference functions on an objective task field. This paper describes a framework for understanding conversational language, using the multi-paradigm knowledge representation (frames" and rules") which represents concept hierarchy and causal relationships for an objective field. Knowledge of the objective field is used in the process to interpret input sentences as a model for the objective world. In interpreting sentences, a procedure judges preferences for interpretation candidates by identifying causal relationship with messages in the preceding context, where the causal relationship is used to supplement some shortage of information and to give either an affirmative or a negative explanation to the interpretation. The procedure has been implemented in an experimental question-answering system, whose current task is consultation in operating an electronic device. The experimental results are shown for a concrete problem involving resolving anaphoric references, and characteristics of the knowledge processing system are discussed.