This traditional view focuses on deriving meaning, and rests on the assumption that the syntactic, semantic, pragmatic, and world knowledge employed in this derivation is “crisp” as opposed to probabilistic; i.e., the distributional properties of language are a mere byproduct of linguistic communication, rather than an essential factor in language understanding, use, or even learning. Thus the emphasis, in this view, is on formulating nonprobabilistic syntactic, semantic, pragmatic, and KR theories to be deployed in language understanding and use. Of course, the problem of ambiguity has always been a focal issue in building parsers and language understanding systems, but the prevailing assumption was that ambiguity resolution could be accomplished by supplementing the interpretive routines with some carefully formulated heuristics expressing syntactic and semantic preferences.