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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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An open-source framework for large-scale, flexible evaluation of biomedical text mining systems.

William A Baumgartner1, K Bretonnel Cohen, Lawrence Hunter

  • 1Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA. larry.hunter@uchsc.edu.

Journal of Biomedical Discovery and Collaboration
|January 31, 2008
PubMed
Summary

A new framework enables large-scale evaluation of text mining technologies in biomedicine. Structured evaluations revealed how gene mention recall impacts gene normalization performance.

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Area of Science:

  • Biomedical informatics
  • Computational linguistics
  • Natural language processing

Background:

  • Improved evaluation methodologies are crucial for advancing text mining.
  • Current practices lack structured, large-scale evaluation capabilities.
  • This study addresses the need for a robust evaluation framework.

Purpose of the Study:

  • To present a publicly available framework for evaluating text mining technologies.
  • To demonstrate the framework's extensibility and utility in the biomedical domain.
  • To facilitate structured, large-scale system assessments.

Main Methods:

  • The evaluation framework was built using the Unstructured Information Management Architecture (UIMA).
  • Experiment 1 analyzed 225 combinations of gene mention identification systems, corpora, and correctness measures.
  • Experiment 2 assessed gene normalization performance across 4,097 gene mention system combinations.

Main Results:

  • Interactions between system, corpus, and measure significantly affected system rankings in gene mention identification.
  • Gene mention system recall, not precision, was the primary driver of gene normalization performance.
  • Effective gene normalization was achieved even with low gene mention system precision.

Conclusions:

  • Structured evaluation of biomedical language processing systems can lead to novel discoveries.
  • The framework promotes collaboration among developers in the biomedical domain.
  • The codebase is publicly available via the BioNLP UIMA Component Repository.