ArticleJournal of the American Medical Informatics Association : JAMIA
Exploiting domain information for Word Sense Disambiguation of medical documents.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text.Journal of biomedical informatics · 2013Article
- A novel approach to word sense disambiguation based on topical and semantic association.TheScientificWorldJournal · 2013Article
- A learning-based approach for biomedical word sense disambiguation.TheScientificWorldJournal · 2012Article
- Combining corpus-derived sense profiles with estimated frequency information to disambiguate clinical abbreviations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
3 authors.
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Abstract
objectiveCurrent techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the domain of the target documents. The authors' goal is to improve these methods by using information about the topic of the document in which the ambiguous term appears.
designThe authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context. MEASUREMENTS: A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset. RESULTS AND DISCUSSION: The authors report a significant improvement when combining those key terms with local context, showing that domain information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency.
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