title: Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text.
abstract: BACKGROUND
  In 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.
  
  
  METHODS
  We evaluate our method on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms.
  
  
  RESULTS
  We 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.
  
  
  BACKGROUND
  The 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.
authors.affiliation: Minnesota Supercomputing Institute, University of Minnesota, 117 Pleasant St SE, Minneapolis, MN 55455. Electronic address: btmcinnes@gmail.com.
authors.forename: Bridget T
authors.initials: BT
authors.lastname: McInnes
authors.forename: Ted
authors.initials: T
authors.lastname: Pedersen
mesh_terms: D005069:Evaluation Studies as Topic; D012660:Semantics; D017432:Unified Medical Language System
keywords: Biomedical documents; NLP; Natural language processing; Semantic similarity and relatedness; WSD; Word sense disambiguation