An empirically supported approach to the treatment of imprecision in vague reasoning.

The aim of this paper is to propose a new approach for the automatic treatment of linguistic vagueness. Our motivation is the feeling that most existing approaches dealing with linguistic information are based on converting vague meaning into crisp meaning using some conversions to precise measurements. As a result, existing approaches are adequate and easy to implement, but do not closely model the human thought process. To help alleviate this deficiency, we propose the use of linguistic relations to provide a natural language interface to an end user. We show a possible linguistic Prolog model based on an extension of the syntactic unification algorithm using synonymy and antonymy, as well as the extension of the resolution principle. Our approach does not aim to provide a well-founded formal semantics for such a linguistic Prolog, but a simple model supported by two experiments focused on the use of vague language, both of them executed in Spanish (an analysis of the data of the first experiment it is also available in that language at [1]). Thus, the purpose of this paper is to contribute to the mechanization of approximate reasoning by being respectful of the semantics of the vague terms involved in it; i.e., by paying attention to how they are evaluated by linguistic users under experimentation. [ABSTRACT FROM AUTHOR]