Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 46, Issue 6, Pages 1116-1124Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2013.08.008
Keywords
Natural language processing; NLP; Word sense disambiguation; WSD; Semantic similarity and relatedness; Biomedical documents
Funding
- National Institute of Health, National Library of Medicine Grant [R01 LM009623-01]
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Introduction: 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. Method: 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. Availability: 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. (C) 2013 Elsevier Inc. All rights reserved.
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