The paper discusses the origins and structure of Preference Semantics, a procedural and computational system for extracting the meaning structure of natural language texts, based on notions of “maximal semantic density” and coherence. The basic representational structures and procedures of Preference Semantics are described, as well as the forms these notions have taken in the work of others.

This dissertation describes a computational system for the analysis of English prose under the Preference Semantics theory of language understanding. The two main areas of investigation are these: (1) the design and implementation of a series of programs for extracting semantic information from a machine-readable dictionary, with this semantic information in a form suitable for use by a subsequent semantic analysis program, and (2) the design and implementation of a semantic analysis program that accepts short English texts and creates a corresponding representation from them. The resulting representation is in a suitable form, such that other Artificial Intelligence programs can use it as a knowledge source.

Artificial Intelligence 6:53-74.