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A novel framework for biomedical entity sense induction.

J A Lossio-Ventura1, J Bian1, C Jonquet2

  • 1College of Medicine, University of Florida, USA.

Journal of Biomedical Informatics
|June 24, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for biomedical entity sense induction, improving the organization of vast biomedical research data. The developed methods effectively identify and disambiguate biomedical terms, aiding knowledge base construction.

Keywords:
BioNLPBiomedicineClassificationClusteringNumber of cluster predictionPolysemy detectionWord sense induction

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Biomedical research generates a large volume of unstructured online data.
  • Organizing and navigating this information is challenging.
  • Ontologies and knowledge bases (KBs) can aid organization, but require accurate word sense identification.

Purpose of the Study:

  • To develop and evaluate a framework for biomedical entity sense induction.
  • To address the challenge of identifying word senses in biomedical texts for improved information organization.

Main Methods:

  • A three-step framework combining natural language processing, supervised, and unsupervised learning.
  • Polysemy detection to identify ambiguous biomedical entities.
  • Clustering quality index to predict the number of senses and induce concepts.

Main Results:

  • Achieved 98% F-measure for polysemy detection.
  • Achieved 93% F-measure for predicting the number of senses.
  • Successfully induced biomedical entity concepts using clustering and keyword extraction.

Conclusions:

  • A novel framework for biomedical entity sense induction has been developed with high accuracy.
  • This framework can resolve concept ambiguities in biomedical text.
  • Facilitates the construction of Semantic Web KBs from biomedical literature.