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ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-05082023-105614


Thesis type
Tesi di dottorato di ricerca
Author
MOLINARI, ALESSIO
URN
etd-05082023-105614
Thesis title
Posterior Probabilities, Active Learning, and Transfer Learning in Technology-Assisted Review
Academic discipline
INF/01
Course of study
INFORMATICA
Supervisors
tutor Dott. Sebastiani, Fabrizio
supervisore Dott. Esuli, Andrea
Keywords
  • deep learning
  • machine learning
  • sld
  • technology-assisted review
  • transfer learning
Graduation session start date
17/05/2023
Availability
Full
Summary
Technology-Assisted Review (TAR) refers to the human-in-the-loop machine learning process whose goal is that of maximizing the cost-effectiveness of a review (i.e., the task of labeling items to satisfy an information need). This thesis explores and thoroughly analyzes: the applicability of the SLD algorithm to TAR scenarios; the usage of active learning combined with the MINECORE framework, effectively improving the framework performance; the portability of machine/deep learning models for the production of systematic reviews in empirical medicine. Finally, the thesis proposes a new algorithm, based on SLD, called SALt, which improves the class prevalence estimates on active learning scenarios, with respect to the current state-of-the-art.
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