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Open Access Open Badges Research article

Using contextual and lexical features to restructure and validate the classification of biomedical concepts

Jung-Wei Fan, Hua Xu and Carol Friedman*

Author Affiliations

Department of Biomedical Informatics, Columbia University Vanderbilt Clinic, 5th Floor, 622 West 168th Street, New York, NY 10032, USA

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BMC Bioinformatics 2007, 8:264  doi:10.1186/1471-2105-8-264

Published: 24 July 2007



Biomedical ontologies are critical for integration of data from diverse sources and for use by knowledge-based biomedical applications, especially natural language processing as well as associated mining and reasoning systems. The effectiveness of these systems is heavily dependent on the quality of the ontological terms and their classifications. To assist in developing and maintaining the ontologies objectively, we propose automatic approaches to classify and/or validate their semantic categories. In previous work, we developed an approach using contextual syntactic features obtained from a large domain corpus to reclassify and validate concepts of the Unified Medical Language System (UMLS), a comprehensive resource of biomedical terminology. In this paper, we introduce another classification approach based on words of the concept strings and compare it to the contextual syntactic approach.


The string-based approach achieved an error rate of 0.143, with a mean reciprocal rank of 0.907. The context-based and string-based approaches were found to be complementary, and the error rate was reduced further by applying a linear combination of the two classifiers. The advantage of combining the two approaches was especially manifested on test data with sufficient contextual features, achieving the lowest error rate of 0.055 and a mean reciprocal rank of 0.969.


The lexical features provide another semantic dimension in addition to syntactic contextual features that support the classification of ontological concepts. The classification errors of each dimension can be further reduced through appropriate combination of the complementary classifiers.