A basic idea suggested in this paper is that a linguistic hedge such as very, more or less, much, essentially. slightly, etc. may be viewed as an operator which acts on the fuzzy set representing the meaning of its operand. For example, in the case of the composite term very tall man, the operator very acts on the fuzzy meaning of the term tall man. To represent a hedge as an operator, it is convenient to define several elementary operations on fuzzy sets from which more complicated operations may be built up by combination or composition. In this way, an approximate representation for a hedge can be expressed in terms of such operations as complementation, intersection, concentration, dilation, contrast intensification, fuzzification, accentuation, etc. Two categories of hedges are considered. In the case of hedges of Type I, e.g., very, much, more or less, slightly, etc., the hedge can be approximated by an operator acting on a single fuzzy set. In the case of hedges of Type II, e.g., technical...

A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges

PRUF—an acronym for Possibilistic Relational Universal Fuzzy—is a meaning representation language for natural languages which departs from the conventional approaches to the theory of meaning in several important respects. First, a basic assumption underlying PRUF is that the imprecision that is intrinsic in natural languages is, for the most part, possibilistic rather than probabilistic in nature. Thus, a proposition such as “Richard is tall” translates in PRUF into a possibility distribution of the variable Height (Richard), which associates with each value of the variable a number in the interval [0,11 representing the possibility that Height (Richard) could assume the value in question. More generally, a proposition, p , translates into a procedure, P, which returns a possibility distribution, Π p , with P and Π p representing, respectively, the meaning of p and the information conveyed by p . In this sense, the concept of a possibility distribution replaces that of truth as a foundation for the representation of meaning in natural languages. Second, the logic underlying PRUF is not a two-valued or multivalued logic, but a fuzzy logic, FL, in which the truth-values are linguistic, that is, are of the form true, not true, very true, more or less true, not very true , etc., with each such truth-value representing a fuzzy subset of the unit interval. The truth-value of a proposition is defined as its compatibility with a reference proposition, so that given two propositions p and r, one can compute the truth of p relative to r . Third, the quantifiers in PRUF—like the truth-values—are allowed to be linguistic, i.e. may be expressed as most, many, few, some, not very many, almost all, etc. Based on the concept of the cardinality of a fuzzy set, such quantifiers are given a concrete interpretation which makes it possible to translate into PRUF propositions exemplified by “Many tall men are much taller than most men,” “All tall women are blonde is not very true,” etc. The translation rules in PRUF are of four basic types: Type I—pertaining to modification; Type II—pertaining to composition; Type III—pertaining to quantification; and Type IV—pertaining to qualification and, in particular, to truth qualification, probability qualification and possibility qualification. The concepts of semantic equivalence and semantic entailment in PRUF provide a basis for question-answering and inference from fuzzy premises. In addition to serving as a foundation for approximate reasoning, PRUF may be employed as a language for the representation of imprecise knowledge and as a means of precisiation of fuzzy propositions expressed in a natural language.

PRUF—a meaning representation language for natural languages

Quantitative fuzzy semantics

Fuzzy logic differs from conventional logical systems in that it aims at providing a model for approximate rather than precise reasoning.

Local and fuzzy logics

Source model extraction---the automated extraction of information from system artifacts---is a common phase in reverse engineering tools. One of the major challenges of this phase is creating extractors that can deal with irregularities in the artifacts that are typical for the reverse engineering domain (for example, syntactic errors, incomplete source code, language dialects and embedded languages). This paper proposes a solution in the form of emph{island grammars, a special kind of grammars that combine the detailed specification possibilities of grammars with the liberal behavior of lexical approaches. We show how island grammars can be used to generate robust parsers that combine the accuracy of syntactical analysis with the speed, flexibility and tolerance usually only found in lexical analysis. We conclude with a discussion of the development of Mangrove, a generator for source model extractors based on island grammars and describe its application to a number of case studies.

Generating robust parsers using Island grammars

: A fuzzy language is defined to be a fuzzy subset of the set of strings over a finite alphabet. The notions of union, intersection, concatenation, Kleene closure and grammar for such languages are defined as extensions of the corresponding notions in the theory of formal languages. An explicit expression for the membership function of the language L(G) generated by a fuzzy grammar G is given and it is shown that any context-sensitive fuzzy grammar is recursive. For fuzzy context-free grammars, procedures for constructing the Chomsky and Greibach normal forms are outlined and illustrated by examples. (Author)

E. T. Lee

Papers

Note on fuzzy languages

Note on fuzzy languages