Given a biomedical research question, formalizing a query for retrieving relevant evidence from Biomedical data-sets is an important methodological step in conducting literature reviews and has been the topic of a number of works on technology-assisted reviews. In this paper, we provide a deep structure that learns to effectively generate terms for expanding Biomedical queries for searching over the data-sets referenced by PubMed, the major search system for Biomedical studies. We use and fine-tune BERT for modelling contextual relationship among terms in the textual content. We evaluate our work on the CORD-19 data-set and show its effectiveness compared with the baselines
Article ID: 2022L24
Month: May
Year: 2022
Address: Online
Venue: Canadian Conference on Artificial Intelligence
Publisher: Canadian Artificial Intelligence Association
URL: https://caiac.pubpub.org/pub/jzs7evcl