Test-Score Semantics for Natural Languages and Meaning Representation via PRUF


Zadeh, L. A. (1982). Test-score semantics for natural languages. In Coling 1982: Proceedings of the Ninth International Conference on Computational Linguistics.


In a sharp departure from conventional approaches to the problem of meaning representation in natural languages, test-score semantics is based on the premise that almost everything that relates to natural language is a matter of degree. Thus, in test-score semantics, predicates, propositions and other types of linguistic entities are treated as collections of elastic constraints on a set of objects or relations in a universe of discourse.

Viewed in this perspective, the meaning of a linguistic entity may be defined by: (a) identifying the constraints which are implicit or explicit in the entity in question(b) describing the tests that must be performed to ascertain the degree to which each constraint is satisfied; and (c) specifying the manner in which the degrees in question or equivalently, the partial test scores are to be aggregated to yield an overall test score. In general, the overall test score is a vector whose components are numbers int he until interval or possibility/probability distributions over this interval. 

The first step in the representation of the meaning of a given proposition involves the construction of a relational database in which the meaning of constituent relations and their attributes is assumed to be known. The choice of the database affects the explanatory effectiveness of the translation process and is governed by the knowledge profile of the intended user of the translation. The test procedure- which is regarded as the representation of the meaning of the proposition- acts on the database and returns an overall test score which is interpreted as the compatibility of p with the database. 

Test-score semantics is sufficiently general to allow the translation into PRUF of almost any proposition in a natural language. However, the price of generality is the difficulty of writing a program which could represent the meaning of a given proposition without recourse to human assistance.

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