Acquiring New Words from Context

Abstract

We present an incremental approach to the task of learning word definitions, including both syntactic and semantic information, from the use of unknown words in context. It is implemented in a unification-based natural language system called LINK. The approach naturally fits into LINK's normal language processing. Unification operations, applied routinely during processing, provide the learning algorithm with both syntactic and semantic information, which it uses to formulate hypotheses about word definitions. We describe the LINK system and the learning algorithm, and present the results of an empirical test, in which the algorithm was used on a limited-domain application corpus.