APROBABILISTIC SYSTEM FOR NATURAL LANGUAGE PROCESSING

摘要

A natural language understanding is described which provides for the generation of concept codes from free-text medical data. A probabilistic model of lexical semantics, in the preferred embodiment of the invention implemented by means of a Bayesian network, is used to determine the most probable concept or meaning associated with a sentence or phrase. The inventive method and system include the steps of checking for synonyms (301), checking spelling (302), performing syntactic parsing (303), transforming text into its "deep" or semantic form (304), and performing a semantic analysis based on a probabilistic model of lexical semantics (305). In the preferred embodiment of the invention, spell checking and transformational processing as well as semantic analysis make use of semantic probabilistic determinations.