This paper explores the use of Concept Unique Identifiers (CUIs) as assigned by MetaMap as features for a supervised learning approach to word sense disambiguation of biomedical text. We compare the use of CUIs that occur in abstracts containing an instance of the target word with using the CUIs that occur in sentences containing an instance of the target word. We also experiment with frequency cutoffs for determining which CUIs should be included as features. We find that a Naive Bayesian classifier where the features represent CUIs that occur two or more times in abstracts containing the target word attains accuracy 9% greater than Leroy and Rindflesch’s approach, which includes features based on semantic types assigned by MetaMap. Our results are comparable to those of Joshi, et. al. and Liu, et. al., who use feature sets that do not contain biomedical information
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