Lexical Knowledge Representation and Natural Language Processing

Abstract Traditionally, semantic information in computational lexicons is limited to notions such as selectional restrictions or domain-specific constraints, encoded in a “static” representation. This information is typically used in natural language processing by a simple knowledge manipulation mechanism limited to the ability to match valences of structurally related words. The most advanced device for imposing structure on lexical information is that of inheritance, both at the object (lexical items) and meta (lexical concepts) levels of lexicon. In this paper we argue that this is an impoverished view of a computational lexicon and that, for all its advantages, simple inheritance lacks the descriptive power necessary for characterizing fine-grained distinctions in the lexical semantics of words. We describe a theory of lexical semantics making use of a knowledge representation framework that offers a richer, more expressive vocabulary for lexical information. In particular, by performing specialized inference over the ways in which aspects of knowledge structures of words in context can be composed, mutually compatible and contextually relevant lexical components of words and phrases are highlighted. We discuss the relevance of this view of the lexicon, as an explanatory device accounting for language creativity, as well as a mechanism underlying the implementation of open-ended natural language processing systems. In particular, we demonstrate how lexical ambiguity resolution—now an integral part of the same procedure that creates the semantic interpretation of a sentence itself—becomes a process not of selecting from a pre-determined set of senses, but of highlighting certain lexical properties brought forth by, and relevant to, the current context.