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Learning Boolean queries for article quality filtering.

Yin Aphinyanaphongs1, Constantin F Aliferis

  • 1Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. ping.pong@vanderbilt.edu

Studies in Health Technology and Informatics
|September 14, 2004
PubMed
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This study introduces decision trees to create Boolean query filters for internal medicine articles, overcoming limitations of Support Vector Machine models. These filters are human-validated and compatible with Boolean search engines.

Area of Science:

  • Medical Informatics
  • Information Retrieval

Background:

  • Support Vector Machine (SVM) models effectively identify high-quality internal medicine articles.
  • SVM models present limitations: incompatibility with Boolean search engines and lack of human verifiability.

Purpose of the Study:

  • To develop an alternative method for identifying high-quality internal medicine articles.
  • To generate human-inspectable and Boolean-search-engine-compatible query filters.

Main Methods:

  • Utilized decision trees combined with feature selection methods.
  • Automated generation of decision trees for the internal medicine domain.
  • Developed Boolean query filters based on the generated decision trees.

Main Results:

Related Experiment Videos

  • Decision trees exhibited high performance in identifying relevant articles.
  • Generated Boolean query filters are understandable, manageable, and human-validatable.
  • The Boolean queries are sensible and directly usable in Boolean search engines.

Conclusions:

  • Decision trees offer a viable and advantageous alternative to SVM models for this task.
  • The developed method enhances the usability and transparency of content-specific article retrieval in internal medicine.