Ad-hoc Biomedical Information Retrieval for Global Pandemics: A Study of Methods Based on the TREC-COVID test collection

The TREC_COVID Challenge has the goal to create search engines to effectively and efficiently retrieve information produced at a rate never seen before, in the biomedical field. This work focuses on the effectiveness of the information retrieval. The search engine is based on Elasticsearch. A multitude of information retrieval techniques are tested, with the goal of identifying the ones leading to a performance improvement. The techniques' effectiveness is measured using the evaluation measures: P@20, MAP, and BPref. The techniques explored that yield improvement in the search are: custom analyzers, filters, relevance feedback and reciprocal rank fusion. Other tested techniques, that yield negligible results, are: field boosting, bigrams and distance feature. Ultimately, the results are compared to the ones obtained by others in the Challenge.