SemEval-2015 Task 14: Analysis of Clinical Text

The purpose of this task is to enhance current research in natural language processing methods used in the clinical domain. The second aim of the task is to introduce clinical text processing to the broader NLP community. The task aims to combine supervised methods for text analysis with unsupervised approaches. More specifically, the task aims to combine supervised methods for entity/acronym/abbreviation recognition and mapping to UMLS CUIs (Concept Unique Identifiers) with access to larger clinical corpus for utilizing unsupervised techniques. It also comprises the task of identifying various attributes of the disorders and normalizing their values. We refer to this as the template filling task.

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Organizers (in alphabetical order)

  • Wendy W. Chapman, University of Utah
  • Noemie Elhadad, Columbia University
  • Suresh Manandhar, University of York, UK
  • Sameer S. Pradhan, Harvard University
  • Guergana K. Savova, Harvard University

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