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Topic modeling enhances automatic citation screening by representing studies as topic distributions, outperforming traditional bag-of-words methods for systematic reviews.

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Area of Science:

  • Information Science
  • Computer Science
  • Bibliometrics

Background:

  • Systematic review screening is labor-intensive and costly.
  • Machine learning and text mining offer potential to automate study identification.
  • Current methods often use a bag-of-words (BOW) model.

Purpose of the Study:

  • To explore topic modeling for improved study representation in systematic reviews.
  • To enhance automatic citation screening efficiency and accuracy.

Main Methods:

  • Applied Latent Dirichlet Allocation (LDA), an unsupervised topic modeling approach.
  • Represented studies as distributions of LDA topics.
  • Enriched LDA topics with terms identified by an automatic term recognition (ATR) tool.
  • Evaluated using Support Vector Machine (SVM) classifiers comparing LDA and BOW representations.

Main Results:

  • The LDA topic-based representation significantly improved SVM classifier performance over BOW.
  • This enhanced performance was observed across clinical and social science systematic reviews.
  • The term-enriched LDA topics proved more informative.

Conclusions:

  • A topic-based document representation is superior to BOW for automatic citation screening.
  • Term-enriched topics offer greater informativeness and reduced ambiguity for reviewers.