Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the UMLS. Academic Article Article uri icon

Overview

MeSH

  • MEDLINE
  • Terminology as Topic

MeSH Major

  • Abbreviations as Topic
  • Artificial Intelligence
  • Unified Medical Language System

abstract

  • Motivation. The UMLS has been used in natural language processing applications such as information retrieval and information extraction systems. The mapping of free-text to UMLS concepts is important for these applications. To improve the mapping, we need a method to disambiguate terms that possess multiple UMLS concepts. In the general English domain, machine-learning techniques have been applied to sense-tagged corpora, in which senses (or concepts) of ambiguous terms have been annotated (mostly manually). Sense disambiguation classifiers are then derived to determine senses (or concepts) of those ambiguous terms automatically. However, manual annotation of a corpus is an expensive task. We propose an automatic method that constructs sense-tagged corpora for ambiguous terms in the UMLS using MEDLINE abstracts. For a term W that represents multiple UMLS concepts, a collection of MEDLINE abstracts that contain W is extracted. For each abstract in the collection, occurrences of concepts that have relations with W as defined in the UMLS are automatically identified. A sense-tagged corpus, in which senses of W are annotated, is then derived based on those identified concepts. The method was evaluated on a set of 35 frequently occurring ambiguous biomedical abbreviations using a gold standard set that was automatically derived. The quality of the derived sense-tagged corpus was measured using precision and recall. The derived sense-tagged corpus had an overall precision of 92.9% and an overall recall of 47.4%. After removing rare senses and ignoring abbreviations with closely related senses, the overall precision was 96.8% and the overall recall was 50.6%. UMLS conceptual relations and MEDLINE abstracts can be used to automatically acquire knowledge needed for resolving ambiguity when mapping free-text to UMLS concepts.

publication date

  • November 2002
  • December 2002

has subject area

  • Abbreviations as Topic
  • Artificial Intelligence
  • MEDLINE
  • Terminology as Topic
  • Unified Medical Language System

Research

keywords

  • Evaluation Studies
  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC349379

PubMed ID

  • 12386113

Additional Document Info

start page

  • 621

end page

  • 636

volume

  • 9

number

  • 6