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. 2022 Mar 15:240:108072.
doi: 10.1016/j.knosys.2021.108072. Epub 2021 Dec 31.

Information retrieval and question answering: A case study on COVID-19 scientific literature

Arantxa Otegi  1 , Iñaki San Vicente  2 , Xabier Saralegi  2 , Anselmo Peñas  3 , Borja Lozano  3 , Eneko Agirre  1
Affiliations

Information retrieval and question answering: A case study on COVID-19 scientific literature

Arantxa Otegi et al. Knowl Based Syst. .

Abstract

Biosanitary experts around the world are directing their efforts towards the study of COVID-19. This effort generates a large volume of scientific publications at a speed that makes the effective acquisition of new knowledge difficult. Therefore, Information Systems are needed to assist biosanitary experts in accessing, consulting and analyzing these publications. In this work we develop a study of the variables involved in the development of a Question Answering system that receives a set of questions asked by experts about the disease COVID-19 and its causal virus SARS-CoV-2, and provides a ranked list of expert-level answers to each question. In particular, we address the interrelation of the Information Retrieval and the Answer Extraction steps. We found that a recall based document retrieval that leaves to a neural answer extraction module the scanning of the whole documents to find the best answer is a better strategy than relying in a precise passage retrieval before extracting the answer span.

Keywords: 00-01; 99-00; COVID-19; Question answering.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
NDNS-Relaxed results (y-axis) of the exploration for each of the values of the hyperparameters.
Fig. 2
Fig. 2
NDNS-Relaxed results for different values of k in linear combination.
See this image and copyright information in PMC

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