title: Hybrid methods for improving information access in clinical documents: concept, assertion, and relation identification.
abstract: OBJECTIVE
  This paper describes the approaches the authors developed while participating in the i2b2/VA 2010 challenge to automatically extract medical concepts and annotate assertions on concepts and relations between concepts.
  
  
  METHODS
  The authors'approaches rely on both rule-based and machine-learning methods. Natural language processing is used to extract features from the input texts; these features are then used in the authors' machine-learning approaches. The authors used Conditional Random Fields for concept extraction, and Support Vector Machines for assertion and relation annotation. Depending on the task, the authors tested various combinations of rule-based and machine-learning methods.
  
  
  RESULTS
  The authors'assertion annotation system obtained an F-measure of 0.931, ranking fifth out of 21 participants at the i2b2/VA 2010 challenge. The authors' relation annotation system ranked third out of 16 participants with a 0.709 F-measure. The 0.773 F-measure the authors obtained on concept extraction did not make it to the top 10.
  
  
  CONCLUSIONS
  On the one hand, the authors confirm that the use of only machine-learning methods is highly dependent on the annotated training data, and thus obtained better results for well-represented classes. On the other hand, the use of only a rule-based method was not sufficient to deal with new types of data. Finally, the use of hybrid approaches combining machine-learning and rule-based approaches yielded higher scores.
authors: LIMSI-CNRS, Orsay Cedex, France.
authors: Anne-Lyse
authors: AL
authors: Minard
authors: Anne-Laure
authors: AL
authors: Ligozat
authors: Asma
authors: A
authors: Ben Abacha
authors: Delphine
authors: D
authors: Bernhard
authors: Bruno
authors: B
authors: Cartoni
authors: Louise
authors: L
authors: Deléger
authors: Brigitte
authors: B
authors: Grau
authors: Sophie
authors: S
authors: Rosset
authors: Pierre
authors: P
authors: Zweigenbaum
authors: Cyril
authors: C
authors: Grouin
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