title: Word sense disambiguation via semantic type classification.
abstract: Accurate concept identification is crucial to biomedical natural language processing. However,ambiguity is common during the process of mapping terms to biomedical concepts (one term can be mapped to several concepts). A cost-effective approach to disambiguation relating to training is via semantic classification of the ambiguous terms,provided that the semantic classes of the concepts are available and are all different. We propose such a semantic classification based method to disambiguate ambiguous mappings with different semantic type(s), which can be used with any program that maps terms to UMLS concepts.Classifiers for the semantic types were built using abundant features extracted from a huge corpus with terms mapped to UMLS concepts. The method achieved a precision of 0.709, with unique advantages not achievable by the other comparable methods. Our results also demonstrate a need to further investigate the complementary properties of different methods.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Jung-Wei
authors.initials: JW
authors.lastname: Fan
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics; D018875:Vocabulary, Controlled