Unpacking meaning from words: A context-centered approach to computational lexicon design

Abstract

The knowledge representation tradition in computational lexicon design represents words as static encapsulations of purely lexical knowledge. We suggest that this view poses certain limitations on the ability of the lexicon to generate nuance-laden and context-sensitive meanings, because word boundaries are obstructive, and the impact of non-lexical knowledge on meaning is unaccounted for. Hoping to address these problematics, we explore a context-centered approach to lexicon design called a Bubble Lexicon. Inspired by Ross Quillian's Semantic Memory System, we represent word-concepts as nodes on a symbolic-connectionist network. In a Bubble Lexicon, a word's meaning is defined by a dynamically grown context-sensitive bubble; thus giving a more natural account of systematic polysemy. Linguistic assembly tasks such as attribute attachment are made context-sensitive, and the incorporation of general world knowledge improves generative capability. Indicative trials over an implementation of the Bubble Lexicon lends support to our hypothesis that unpacking meaning from predefined word structures is a step toward a more natural handling of context in language.