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Symbolic meanings are made precise through some systematic organization, which may be a simple catalog of distinct meanings or a formal definition using a set of axioms. For example, the verb treat (and its variant forms treats, treated, and treating) might be assigned the symbolic meaning THERAPEUTIC-ACTIVITY, while the noun form would not have a semantic representation in the medical domain.
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Natural language processing systems represent the meaning of a given word or phrase using a symbol or code. The computer cannot make use of textual dictionary definitions, but instead requires a semantic representation that is simpler and more precise. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 While there are many important issues in the processing of medical text (sentence parsing, discourse analysis, document structure), 12, 13, 14 one of the most fundamental issues is how the computer represents the meanings of individual words and phrases.įor humans, the meaning of a given word can be obtained by consulting a dictionary. The techniques of natural language processing can be applied to transform medical narrative into a form more suitable for information processing and management. While textual data are convenient for tasks such as review by clinicians, they present significant obstacles for graphic presentation, searching, summarization, and statistical analysis. Much of the available data are in textual form as a result of transcription of dictated reports, use of speech recognition technology, and direct entry by health care providers. Increasingly, medical institutions have access to patient records through computers. Further work is needed to increase the coverage of the semantic lexicon and to exploit contextual information when selecting semantic senses. Semantic preference rules can be used to select semantic types that are appropriate to clinical reports. This semantic information can aid natural language processing programs that analyze medical narrative, provided that lexemes with multiple semantic types are kept to a minimum. In the discharge summaries, occurrences of lexemes with multiple semantic types were reduced from 9.41 to 1.46 percent.Ĭonclusion: Automatic methods can be used to construct a semantic lexicon from existing UMLS sources. When semantic preference rules were applied to the semantic lexicon, the number of entries with multiple semantic types was reduced to 423 (1.5 percent). Of those lexemes in the corpus that had semantic types, 3,474 (12.6 percent) had two or more types. This suggests that the Specialist Lexicon has about 79 percent coverage for syntactic information and 38 percent coverage for semantic information for discharge summaries. Matching the Specialist Lexicon against one year's worth of discharge summaries identified 27,633 distinct lexical forms, 13,322 of which had at least one semantic type. Results: Matching the Specialist Lexicon against the Metathesaurus produced a semantic lexicon with 75,711 lexical forms, 22,805 (30.1 percent) of which had two or more semantic types. Based on this evidence, semantic preference rules were developed to reduce the number of lexemes with multiple semantic types. A concordance program was used to find contrasting contexts for each lexeme that would reflect different semantic senses. Lexical items with multiple semantic types were examined to determine whether some of the types could be eliminated, on the basis of usage in discharge summaries. The semantic lexicon was then used to assign semantic types to lexemes occurring in a corpus of discharge summaries (603,306 sentences). This yielded a “semantic lexicon,” in which each lexeme is associated with one or more syntactic types, each of which can have one or more semantic types. Objective: Construction of a resource that provides semantic information about words and phrases to facilitate the computer processing of medical narrative.ĭesign: Lexemes (words and word phrases) in the Specialist Lexicon were matched against strings in the 1997 Metathesaurus of the Unified Medical Language System (UMLS) developed by the National Library of Medicine.