Abstract

A robust Natural Language Processing (NLP) system must be able to automatically acquire the syntax and semantics of unknown words that it encounters during processing. It is inevitable that a real--world NLP system will encounter unknown words since the human lexicon continues to grow. This paper describes XXXXX, a system that automatically acquires the meanings of unknown nouns and verbs. XXXXX represents the semantics of nouns and verbs in terms of taxonomies since there is considerable evidence that the human lexicon is largely organized as a taxonomy. The use of taxonomies is common in NLP thus making these methods generally useful. When an unknown word is encountered XXXXX attempts to place the unknown word in a taxonomy. In XXXXX the acquisition of semantics is defined as locating an existing concept node in a concept hierarchy that defines an unknown word. If there is no such node then a node must be created and placed into the concept hierarchy. The former is referred to as the...

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