Using SemRep to label semantic relations extracted from clinical text.

Ying Liu, Robert Bill, Marcelo Fiszman, Thomas Rindflesch, Ted Pedersen, Genevieve B. Melton, Serguei V. Pakhomov

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

In this paper we examined the relationship between semantic relatedness among medical concepts found in clinical reports and biomedical literature. Our objective is to determine whether relations between medical concepts identified from Medline abstracts may be used to inform us as to the nature of the association between medical concepts that appear to be closely related based on their distribution in clinical reports. We used a corpus of 800k inpatient clinical notes as a source of data for determining the strength of association between medical concepts and SemRep database as a source of labeled relations extracted from Medline abstracts. The same pair of medical concepts may be found with more than one predicate type in the SemRep database but often with different frequencies. Our analysis shows that predicate type frequency information obtained from the SemRep database appears to be helpful for labeling semantic relations obtained with measures of semantic relatedness and similarity.

Original languageEnglish (US)
Pages (from-to)587-595
Number of pages9
JournalUnknown Journal
Volume2012
StatePublished - 2012

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