Abstract
In the present scenario COVID-19 pandemic has ruined the entire world. This situation motivates the researchers to resolve the query raised by the people around the world in an efficient manner. However, less number of resources available in order to gain the information and knowledge about COVID-19 arises a need to evaluate the existing Question Answering (QA) systems on COVID-19. In this paper, we compare the various QA systems available in order to answer the questions raised by the people like doctors, medical researchers etc. related to corona virus. QA systems process the queries submitted in natural language to find the best relevant answer among all the candidate answers for the COVID-19 related questions. These systems utilize the text mining and information retrieval on COVID-19 literature. This paper describes the survey of QA systems-CovidQA, CAiRE (Center for Artificial Intelligence Research)-COVID system, CO-search semantic search engine, COVIDASK, RECORD (Research Engine for COVID Open Research Dataset) available for COVID-19. All these QA systems are also compared in terms of their significant parameters-like Precision at rank 1 (P@1), Recall at rank 3(R@3), Mean Reciprocal Rank(MRR), F1-Score, Exact Match(EM), Mean Average Precision, Score metric etc.; on which efficiency of these systems relies.
Highlights
Since the detection of SARS-CoV-2[1] or the Corona virus towards the end of Dec 2019 or the starting of 2020 the lives ofpeople has drastically affected all over the world [2]
This paper describes the survey of Question Answering (QA) systems- CovidQA, CAiRE (Center for Artificial Intelligence Research)COVID system, CO-search semantic search engine, COVIDASK, RECORD (Research Engine for COVID Open Research Dataset) available for COVID-19
QA systems are designed and modeled to process queries by using natural language processors. The performance of these QA systems can be analyzed by using various metrics- like Precision at rank 1 (P@1), Recall at rank 3(R@3), Mean Reciprocal Rank (MRR), F1-Score, Exact Match(EM), Mean Average Precision, Score Metric as discussed
Summary
Since the detection of SARS-CoV-2[1] or the Corona virus towards the end of Dec 2019 or the starting of 2020 the lives ofpeople has drastically affected all over the world [2]. In order to solve these issues various researchers uses the tools from Natural Language Processing, Machine Learning and Artificial Intelligence Using these tools QA systems try to get accurate answers for every different question instead of retrieving appropriate text documents[4]. In this paper we analyze the various question answering systems on COVID-19based on natural language processing like CovidQA, CAiRE (Center for Artificial Intelligence Research)-COVID system, CO-search semantic search engine, COVIDASK, RECORD (Research Engine for COVID Open Research Dataset) etc. These question answering systems are tested using various covid-19 datasets available. The effectiveness of these systems is compared using various metrics like F1-Score, Precision, Recall, Mean reciprocal Rank, opinions and eliminate the words with highest frequency for each opinion, Exact Match (EM) etc
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