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Anaphoric relations in the clinical narrative: corpus creation

Guergana K Savova, Wendy W Chapman, Jiaping Zheng, Rebecca S Crowley
DOI: http://dx.doi.org/10.1136/amiajnl-2011-000108 459-465 First published online: 1 July 2011

Abstract

Objective The long-term goal of this work is the automated discovery of anaphoric relations from the clinical narrative. The creation of a gold standard set from a cross-institutional corpus of clinical notes and high-level characteristics of that gold standard are described.

Methods A standard methodology for annotation guideline development, gold standard annotations, and inter-annotator agreement (IAA) was used.

Results The gold standard annotations resulted in 7214 markables, 5992 pairs, and 1304 chains. Each report averaged 40 anaphoric markables, 33 pairs, and seven chains. The overall IAA is high on the Mayo dataset (0.6607), and moderate on the University of Pittsburgh Medical Center (UPMC) dataset (0.4072). The IAA between each annotator and the gold standard is high (Mayo: 0.7669, 0.7697, and 0.9021; UPMC: 0.6753 and 0.7138). These results imply a quality corpus feasible for system development. They also suggest the complementary nature of the annotations performed by the experts and the importance of an annotator team with diverse knowledge backgrounds.

Limitations Only one of the annotators had the linguistic background necessary for annotation of the linguistic attributes. The overall generalizability of the guidelines will be further strengthened by annotations of data from additional sites. This will increase the overall corpus size and the representation of each relation type.

Conclusion The first step toward the development of an anaphoric relation resolver as part of a comprehensive natural language processing system geared specifically for the clinical narrative in the electronic medical record is described. The deidentified annotated corpus will be available to researchers.

Keywords
  • Clinical natural language processing
  • coreference
  • relation extraction
  • information extraction
  • lexical resources
  • other methods of information extraction
  • natural-language processing
  • monitoring the health of populations
  • knowledge representation
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