A Study on Query Expansion and Rank Fusion for Precision Medicine: The IMS Unipd at TREC 2020 Precision Medicine
In this report, we describe the methodology and the experimental setting of our participation as the IMS Unipd team in TREC PM 2020. The objective of this work is to evaluate a query expansion and ranking fusion approach optimized on the previous years of TREC PM. In particular, we designed a procedure to (1) perform query expansion using a pseudo relevance feedback model on the first k retrieved documents, and (2) apply rank fusion techniques to the rankings produced by the different experimental settings. The results obtained provide interesting insights in terms of the different per topic effectiveness and will be used for further failure analyses.
TREC