Combining contextual and lexical features to classify UMLS concepts.

Jung Wei Fan, Carol Friedman

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)231-235
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - Dec 1 2007
Externally publishedYes

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Unified Medical Language System
Semantics
Terminology

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Combining contextual and lexical features to classify UMLS concepts. / Fan, Jung Wei; Friedman, Carol.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 01.12.2007, p. 231-235.

Research output: Contribution to journalArticle

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