title: Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation.
abstract: OBJECTIVE
  Natural language processing (NLP) tasks are commonly decomposed into subtasks, chained together to form processing pipelines. The residual error produced in these subtasks propagates, adversely affecting the end objectives. Limited availability of annotated clinical data remains a barrier to reaching state-of-the-art operating characteristics using statistically based NLP tools in the clinical domain. Here we explore the unique linguistic constructions of clinical texts and demonstrate the loss in operating characteristics when out-of-the-box part-of-speech (POS) tagging tools are applied to the clinical domain. We test a domain adaptation approach integrating a novel lexical-generation probability rule used in a transformation-based learner to boost POS performance on clinical narratives.
  
  
  METHODS
  Two target corpora from independent healthcare institutions were constructed from high frequency clinical narratives. Four leading POS taggers with their out-of-the-box models trained from general English and biomedical abstracts were evaluated against these clinical corpora. A high performing domain adaptation method, Easy Adapt, was compared to our newly proposed method ClinAdapt.
  
  
  RESULTS
  The evaluated POS taggers drop in accuracy by 8.5-15% when tested on clinical narratives. The highest performing tagger reports an accuracy of 88.6%. Domain adaptation with Easy Adapt reports accuracies of 88.3-91.0% on clinical texts. ClinAdapt reports 93.2-93.9%.
  
  
  CONCLUSIONS
  ClinAdapt successfully boosts POS tagging performance through domain adaptation requiring a modest amount of annotated clinical data. Improving the performance of critical NLP subtasks is expected to reduce pipeline error propagation leading to better overall results on complex processing tasks.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Ferraro
authors.forename: Hal
authors.initials: H
authors.lastname: Daumé
authors.forename: Scott L
authors.initials: SL
authors.lastname: Duvall
authors.forename: Wendy W
authors.initials: WW
authors.lastname: Chapman
authors.forename: Henk
authors.initials: H
authors.lastname: Harkema
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Haug
mesh_terms: D008037:Linguistics; D016347:Medical Records Systems, Computerized; D033262:Narration; D009323:Natural Language Processing
keywords: Clinical Narratives; Domain Adaptation; NLP; Natural Language Processing; POS Tagging