Learning Boolean queries for article quality filtering.

Yin Aphinyanaphongs, Constantin Aliferis

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Prior research has shown that Support Vector Machine models have the ability to identify high quality content-specific articles in the domain of internal medicine. These models, though powerful, cannot be used in Boolean search engines nor can the content of the models be verified via human inspection. In this paper, we use decision trees combined with several feature selection methods to generate Boolean query filters for the same domain and task. The resulting trees are generated automatically and exhibit high performance. The trees are understandable, manageable, and able to be validated by humans. The subsequent Boolean queries are sensible and can be readily used as filters by Boolean search engines.

Original languageEnglish (US)
Title of host publicationStudies in Health Technology and Informatics
Pages263-267
Number of pages5
Volume107
DOIs
StatePublished - 2004

Keywords

  • Artificial Intelligence
  • Information Storage and Retrieval
  • Medical Informatics
  • PubMed
  • Text Categorization

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