title: A classification approach to coreference in discharge summaries: 2011 i2b2 challenge.
abstract: OBJECTIVE
  To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test.
  
  
  METHODS
  An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic extractors and world knowledge, and Pronoun system based on a multi-class support vector machine classifier. The three Person attributes are patient, relative and hospital personnel. Contextual semantic extractors include anatomy, position, medication, indicator, temporal, spatial, section, modifier, equipment, operation, and assertion. The world knowledge is extracted from external resources such as Wikipedia.
  
  
  METHODS
  Micro-averaged precision, recall and F-measure in MUC, BCubed and CEAF were used to evaluate results.
  
  
  RESULTS
  The system achieved an overall micro-averaged precision, recall and F-measure of 0.906, 0.925, and 0.915, respectively, on test data (from four hospitals) released by the challenge organizers. It achieved a precision, recall and F-measure of 0.905, 0.920 and 0.913, respectively, on test data without Pittsburgh data. We ranked the first out of 20 competing teams. Among the four sub-tasks on Person, Problem, Treatment, and Test, the highest F-measure was seen for Person coreference.
  
  
  CONCLUSIONS
  This system achieved encouraging results. The Person system can determine whether personal pronouns and proper names are coreferent or not. The Problem/Treatment/Test system benefits from both world knowledge in evaluating the similarity of two mentions and contextual semantic extractors in identifying semantic clues. The Pronoun system can automatically detect whether a Pronoun mention is coreferent to that of the other four types. This study demonstrates that it is feasible to accomplish the coreference task in discharge summaries.
authors.affiliation: State Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.forename: Jiahua
authors.initials: J
authors.lastname: Liu
authors.forename: Jiajun
authors.initials: J
authors.lastname: Wu
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Zhuowen
authors.initials: Z
authors.lastname: Tu
authors.forename: Jian-Tao
authors.initials: JT
authors.lastname: Sun
authors.forename: Junichi
authors.initials: J
authors.lastname: Tsujii
authors.forename: Eric I-Chao
authors.initials: EI
authors.lastname: Chang
mesh_terms: D001185:Artificial Intelligence; D002965:Classification; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010351:Patient Discharge; D010363:Pattern Recognition, Automated; D012660:Semantics; D060388:Support Vector Machine; D014481:United States