An Empirical Evaluation of Resources for the Identif cation of Diseases and Adverse Effects in Biomedical Literature

نویسندگان

  • Sophia Ananiadou
  • Kevin Cohen
  • Dina Demner-Fushman
  • Pierre Zweigenbaum
  • Haifeng Zheng
  • Harsha Gurulingappa
  • Roman Klinger
  • Martin Hofmann-Apitius
  • Svetla Boytcheva
  • Ivelina Nikolova
  • Elena Paskaleva
  • Galia Angelova
  • Dimitar Tcharaktchiev
  • Nadya Dimitrova
  • Alex Chengyu Fang
  • Jon Patrick
  • Mojtaba Sabbagh
  • Suvir Jain
  • Joanna Karpinski
چکیده

Spelling correction in a large collection of clinical notes is a relatively lesser explored research area and serious problem as typically 30% of tokens are unknown. In this paper we explore techniques to devise a spelling suggestion generator for clinical data with an emphasis on the accuracy of the first suggestion. We use a combination of a rule-based suggestion generation system and a context-sensitive ranking algorithm to obtain the best results. The ranking is achieved using a language model created by bootstrapping from the corpus. The described method is embedded in a practical work-flow that we use for Knowledge Discovery and Reuse in clinical records.

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An Empirical Evaluation of Resources for the Identification of Diseases and Adverse Effects in Biomedical Literature

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تاریخ انتشار 2010