Towards comprehensive syntactic and semantic annotations of the clinical narrative
Open/View files
Date
2013
Author(s)
Albright, Daniel
Lanfranchi, Arrick
Fredriksen, Anwen
Styler, William F
Warner, Colin
Hwang, Jena D
Choi, Jinho D
Nielsen, Rodney D
Martin, James
Ward, Wayne
Palmer, Martha
Publisher
BMJ Publishing Group
The Harvard community has made this article openly available. Please share how this access benefits you.
Citation
Albright, D., A. Lanfranchi, A. Fredriksen, W. F. Styler, C. Warner, J. D. Hwang, J. D. Choi, et al. 2013. “Towards comprehensive syntactic and semantic annotations of the clinical narrative.” Journal of the American Medical Informatics Association : JAMIA 20 (5): 922-930. doi:10.1136/amiajnl-2012-001317. http://dx.doi.org/10.1136/amiajnl-2012-001317.
Abstract
Objective: To create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components. Methods: Manual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for predicate-argument structures, and the Unified Medical Language System (UMLS) schema for semantic information. NLP components were developed. Results: The final corpus consists of 13 091 sentences containing 1772 distinct predicate lemmas. Of the 766 newly created PropBank frames, 74 are verbs. There are 28 539 named entity (NE) annotations spread over 15 UMLS semantic groups, one UMLS semantic type, and the Person semantic category. The most frequent annotations belong to the UMLS semantic groups of Procedures (15.71%), Disorders (14.74%), Concepts and Ideas (15.10%), Anatomy (12.80%), Chemicals and Drugs (7.49%), and the UMLS semantic type of Sign or Symptom (12.46%). Inter-annotator agreement results: Treebank (0.926), PropBank (0.891–0.931), NE (0.697–0.750). The part-of-speech tagger, constituency parser, dependency parser, and semantic role labeler are built from the corpus and released open source. A significant limitation uncovered by this project is the need for the NLP community to develop a widely agreed-upon schema for the annotation of clinical concepts and their relations. Conclusions: This project takes a foundational step towards bringing the field of clinical NLP up to par with NLP in the general domain. The corpus creation and NLP components provide a resource for research and application development that would have been previously impossible.
Other Available Sources
Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service
Collection