Is natural language ever really vague? A computational semantic view

The paper starts out with an observation that, in the domain of fuzzy logic, fuzzy sets, computing with words, etc., the charges from the outside that fuzziness equals probability are routinely and calmly rebuffed, but confusing fuzziness with vagueness has not been ultimately dealt with even inside the community. We leave completely aside the category of vagueness that is an artifact of approaches, both in logic and philosophy as well as trends in linguistics, such as formal semantics, that attempt to apply predicate logic of various flavors and complexity to a limited selection of language phenomena, such as quantifiers and scalars that lend themselves to such a treatment. Instead, using a computational semantic approach based on a language-independent ontology and language-specific lexicons, where each entry is anchored in and defined with the help of ontological properties and concepts, the paper claims that, unlike fuzziness, vagueness is not an inherent feature of certain words, phrases, or sentences. In fact, it is suggested that vagueness does not really exist for a human hearer and thus is just a temporary function of discourse, in which the speaker's grain size level is coarser than that of the hearer. Since hearers handle it routinely by asking for more details, the paper outlines the computational procedure emulating this ability.

I. Introduction

While the focus theme of this conference is very clear, “the man is always in the loop,” even after the full autonomy is achieved: after all, it will have been humans who have designed and written the software. The concern of cybernetics with the human role in computation, expressed very early on, was about the much more immediate human “legacy,” namely natural language. The very first non-number-crunching, symbolic application of the computer was suggested by Norbert Wiener to be machine translation. Thus, natural language was put on the computing agenda from the start. Since then, a large and growing number of natural language processing applications have been developed, demanding higher precision and, hence, increasingly richer resources emulating human knowledge of the world, full competence in natural language, and full comprehension of text, as preconditions for higher precision of the results.