Traditional grammars classify words according to generic syntactic functions or morphological characteristics. For teaching humans and for descriptive linguistics this seemed sufficient. The advent of computers has changed the situation. Since machines are devoid of experiential knowledge, they need a more explicit grammar to handle natural language. Correlational Grammar is an attempt in that direction. The paper describes parts of correlational syntax and shows how a highly differentiated syntax can be used to establish word classes for which an intensional semantic definition can then be found. It exemplifies this approach in two areas of grammar: predicative adjectives and transitive verbs. The classification serves to eliminate ambiguity and spurious computer interpretations of natural language sentences.

Key words: language, semiotics

The paper demonstrates the possibility of deriving, from the Correlational Grammar developed solely for the purpose of automatic sentence analysis, a classification of words that could be useful in language analysis and language teaching. A group of some 90 frequent English adjectives serves as example; they are sorted into ten classes according to their behavior in strings of the type “John is easy to please,"“John is eager to please,"“John is likely to please,” etc. It is suggested that the members of a least some of these classes show common semantic features that could be used to obtain intensional definitions which would theoretically confirm the empirically derived extensional definitions supplied by correlational grammar.

Key words: Adjectives, Discourse Analysis, English, Grammar, Kernel Sentences, Phrase Structure, Semantics, Sentence Structure, Structural Analysis, Structural Grammar, Syntax