title: MCORES: a system for noun phrase coreference resolution for clinical records.
abstract: OBJECTIVE
  Narratives of electronic medical records contain information that can be useful for clinical practice and multi-purpose research. This information needs to be put into a structured form before it can be used by automated systems. Coreference resolution is a step in the transformation of narratives into a structured form.
  
  
  METHODS
  This study presents a medical coreference resolution system (MCORES) for noun phrases in four frequently used clinical semantic categories: persons, problems, treatments, and tests. MCORES treats coreference resolution as a binary classification task. Given a pair of concepts from a semantic category, it determines coreferent pairs and clusters them into chains. MCORES uses an enhanced set of lexical, syntactic, and semantic features. Some MCORES features measure the distance between various representations of the concepts in a pair and can be asymmetric.
  
  
  CONCLUSIONS
  MCORES was compared with an in-house baseline that uses only single-perspective 'token overlap' and 'number agreement' features. MCORES was shown to outperform the baseline; its enhanced features contribute significantly to performance. In addition to the baseline, MCORES was compared against two available third-party, open-domain systems, RECONCILE(ACL09) and the Beautiful Anaphora Resolution Toolkit (BART). MCORES was shown to outperform both of these systems on clinical records.
authors.affiliation: MIT, CSAIL, Cambridge, Massachusetts 02139, USA. andreeab@mit.edu
authors.forename: Andreea
authors.initials: A
authors.lastname: Bodnari
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
authors.forename: Özlem
authors.initials: Ö
authors.lastname: Uzuner
mesh_terms: D001185:Artificial Intelligence; D002965:Classification; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010351:Patient Discharge; D010363:Pattern Recognition, Automated; D012660:Semantics; D014481:United States