PropertyValue
?:abstract
  • With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinicians, researchers, and policy-makers need to be able to search these articles effectively. In this work, we present a zero-shot ranking algorithm that adapts to COVID-related scientific literature. Our approach filters training data from another collection down to medical-related queries, uses a neural re-ranking model pre-trained on scientific text (SciBERT), and filters the target document collection. This approach ranks top among zero-shot methods on the TREC COVID Round 1 leaderboard, and exhibits a P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2 judgments. Despite not relying on TREC-COVID data, our method outperforms models that do. As one of the first search methods to thoroughly evaluate COVID-19 search, we hope that this serves as a strong baseline and helps in the global crisis.
is ?:annotates of
?:arxiv_id
  • 2010.05987
?:creator
?:externalLink
?:license
  • arxiv
?:pdf_json_files
  • document_parses/pdf_json/05598331268614305ff844cea001f5b22f3519c9.json
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • ArXiv
?:title
  • SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
?:type
?:year
  • 2020-10-12

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