Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text

Display Omitted HighlightsObjective: to develop a method that can disambiguate terms in biomedical text.WSD method exploits similarity and relatedness information extracted from the UMLS.Evaluates the efficacy of similarity and relatedness measures on WSD.IC-based measures obtains higher accuracy than path-based and relatedness measures. IntroductionIn this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures. These measures quantify the degree of similarity or relatedness between concepts in the Unified Medical Language System (UMLS). The objective of this work is to develop a method that can disambiguate terms in biomedical text by exploiting similarity and relatedness information extracted from biomedical resources and to evaluate the efficacy of these measure on WSD. MethodWe evaluate our method on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. ResultsWe show that information content-based measures derived from either a corpus or taxonomy obtain a higher disambiguation accuracy than path-based measures or relatedness measures on the MSH-WSD dataset. AvailabilityThe WSD system is open source and freely available from http://search.cpan.org/dist/UMLS-SenseRelate/. The MSH-WSD dataset is available from the National Library of Medicine http://wsd.nlm.nih.gov.