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Semantic classification is important for biomedical terminologies and the many applications that depend on them. Previously we developed two classifiers for 8 broad clinically relevant classes to reclassify and validate UMLS concepts. We found them to be complementary, and then combined them using a manual approach. In this paper, we extended the classifiers by adding an “other” class to categorize concepts not belonging to any of the 8 classes. In addition, we focused on automating the method for combining the two classifiers by training a meta-classifier that performs dynamic combination to exploit the strength of each classifier. The automated method performed as well as manual combination, achieving classification accuracy of about 0.81. |
