Biomedical text disambiguation using UMLS

  • Authors:

  • Wessam Gad El-Rab;Osmar R. Zaïane;Mohammad El-Hajj

  • Affiliations:

  • University of Alberta, Edmonton, Canada;University of Alberta, Edmonton, Canada;MacEwan University, Edmonton, Canada

  • Venue:
  • Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
  • Year:
  • 2013

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Abstract

Interest in extracting information from biomedical documents has increased significantly in recent years but has always been challenged by the ambiguity of natural language. An important source of ambiguity is the usage of polysemous words: words with multiple meanings. Word sense disambiguation algorithms attempt to solve this problem by finding the correct meaning of a polysemous word in a given context, but very few algorithms were designed to disambiguate biomedical text. In this study we propose a word sense disambiguation algorithm focused on biomedical text. The proposed algorithm does not need to be trained and uses a relatively small knowledge base.