Recognizing noun phrases in medical discharge summaries: an evaluation of two natural language parsers

Abstract

We evaluated the ability of two natural language parsers, CLARIT and the Xerox Tagger, to identify simple, noun phrases in medical discharge summaries. In twenty randomly selected discharge summaries, there were 1909 unique simple noun phrases. CLARIT and the Xerox Tagger exactly identified 77.0% and 68.7% of the phrases, respectively, and partially identified 85.7% and 80.8% of the phrases. Neither system had been specially modified or tuned to the medical domain. These results suggest that it is possible to apply existing natural language processing (NLP) techniques to large bodies of medical text, in order to empirically identify the terminology used in medicine. Virtually all the noun phrases could be regarded as having special medical connotation and would be candidates for entry into a controlled medical vocabulary.