An obstacle in Natural Language understanding is the existence of lexical gaps, i.e. words or word senses that are not in the lexicon of the system. This thesis describes the implementation of MURRAY, a learning mechanism which infers the properties of a new lexical item from its syntactical environment and infers its meaning based on context and a domain-specific database of real-world knowledge. It combines these syntactic and semantic properties to result in a version space of possible lexical entries for the unknown. MURRAY is an extension to an existing unification-based grammar processing system, UNICORN. It is compatible with grammars written in the style of head-driven phrase structure grammar. For each word that does not exist in the lexicon, MURRAY constructs a lexical version hyperspace, and then from multiple inputs the system converges on the target definition of the new world. Included in the five appendixes are sample grammars and entries, learning procedure code, and some sample learning episodes. (Contains 64 references.) (Author/SLD)