title: Automating the generation of lexical patterns for processing free text in clinical documents.
abstract: OBJECTIVE
  Many tasks in natural language processing utilize lexical pattern-matching techniques, including information extraction (IE), negation identification, and syntactic parsing. However, it is generally difficult to derive patterns that achieve acceptable levels of recall while also remaining highly precise.
  
  
  METHODS
  We present a multiple sequence alignment (MSA)-based technique that automatically generates patterns, thereby leveraging language usage to determine the context of words that influence a given target. MSAs capture the commonalities among word sequences and are able to reveal areas of linguistic stability and variation. In this way, MSAs provide a systemic approach to generating lexical patterns that are generalizable, which will both increase recall levels and maintain high levels of precision.
  
  
  RESULTS
  The MSA-generated patterns exhibited consistent F1-, F.5-, and F2- scores compared to two baseline techniques for IE across four different tasks. Both baseline techniques performed well for some tasks and less well for others, but MSA was found to consistently perform at a high level for all four tasks.
  
  
  CONCLUSIONS
  The performance of MSA on the four extraction tasks indicates the method's versatility. The results show that the MSA-based patterns are able to handle the extraction of individual data elements as well as relations between two concepts without the need for large amounts of manual intervention.
  
  
  CONCLUSIONS
  We presented an MSA-based framework for generating lexical patterns that showed consistently high levels of both performance and recall over four different extraction tasks when compared to baseline methods.
authors.affiliation: Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA MAVERIC, VA Boston Healthcare System, Boston MA, USA fmeng@mii.ucla.edu.
authors.forename: Frank
authors.initials: F
authors.lastname: Meng
authors.affiliation: Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA Department of Radiology, VA Greater Los Angeles Healthcare System, Los Angeles CA, USA.
authors.forename: Craig
authors.initials: C
authors.lastname: Morioka
mesh_terms: D057225:Data Mining / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: information extraction; natural language processing; text mining