2020/10/01 by Michael Bendersky, Bendersky, Michael, Honglei Zhuang +9 · 4 citations
Biochemistry, Genetics and Molecular Biology · Immunology and Microbiology · #Chromatin Remodeling and Cancer #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #RNA modifications and cancer #interferon and immune responses
paper · pdf · doi:10.48550/arxiv.2010.00200
openalex publication_date 2020/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we report the results of our participation in the TREC-COVID challenge. To meet the challenge of building a search engine for rapidly evolving biomedical collection, we propose a simple yet effective weighted hierarchical rank fusion approach, that ensembles together 102 runs from (a) lexical and semantic retrieval systems, (b) pre-trained and fine-tuned BERT rankers, and (c) relevance feedback runs. Our ablation studies demonstrate the contributions of each of these systems to the overall ensemble. The submitted ensemble runs achieved state-of-the-art performance in rounds 4 and 5 of the TREC-COVID challenge.