ArticleJournal of biomedical informatics2013
Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey.BMC bioinformatics · 2022Article
- snpXplorer: a web application to explore human SNP-associations and annotate SNP-sets.Nucleic acids research · 2021Article
- Polygenic Risk Score of Longevity Predicts Longer Survival Across an Age Continuum.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021Article
- Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- deepBioWSD: effective deep neural word sense disambiguation of biomedical text data.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study.Journal of medical Internet research · 2018Article
- Supervised Learning and Knowledge-Based Approaches Applied to Biomedical Word Sense Disambiguation.Journal of integrative bioinformatics · 2017Article
- Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept EmbeddingsProceedings. IEEE International Symposium on Bioinformatics and Bioengineering · 2017Article
- BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.Bioinformatics (Oxford, England) · 2017Article
- Corpus domain effects on distributional semantic modeling of medical terms.Bioinformatics (Oxford, England) · 2016Article
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Abstract
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. 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.
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