Building an intelligent system for answering specialized questions about COVID-19.

The paper discusses the design and implementation process of an intelligent system for answering specialized questions about COVID-19. The system is based on deep learning and transfer learning techniques and uses the popular CORD-19 dataset as a source of scientific knowledge about the problem domain. The experiments performed with the pilot version of the system are presented and the obtained results are analyzed. Conclusions are formulated about the applicability and the opportunities for improvement of the proposed approach.

Thai Question-Answering System Using Pattem-Matching Approach

Teaching concepts in Thailand's universities have abruptly changed, due to the advancement of the COVID-19 pandemic, including changes in classroom to online formats, as well as administrative difficulties. The research herein, therefore, addresses these concerns, presenting a Thai question-answering system using the pattern-matching approach. Our case study covers course information, teaching timetable, teacher schedule, and course supplements. We classified the questions into six categories according to type and acknowledged typical expressions which matched to question patterns. We use RegEx® to match a defined pattern. When a response did not match, we used word embedding to transform the question into a vector and then calculated the cosine similarity to identify the most similar pattern. The system can then generate a corresponding SQL command to query the answer from the database. We evaluated the accuracy of the proposed system with the collected data resulted in an accuracy rate of 82%. © 2022 IEEE.

Knowledge Retrieval using Question Answering approach on Epidemiology data

The tried and tested way for effective Knowledge Retrieval is by posting questions and retrieving data from the huge information repository. In the recent past the prevalence of pandemics and the spread of COVID-19, has led people to rigorously question the various forms of epidemiology data available on different sources. In general, the amount of information gathered is proportionate to the questioning patterns by the knowledge seeker. Question answering (QA) system is useful during unexpected situations, especially during a pandemic. In this paper, we have proposed a Knowledge Retrieval Question Answering system (KRQA) for answering the queries of users related to COVID-19. The KRQA system is divided into two modules. The first module consists of preprocessing (tokenization, stemming, bag of words) of the question to produce a word vector. The second module involves building, training, and testing the data repository. Feedforward neural network is used to extract the most relevant answer from a repository of all possible answers. The volume and quality of information about the pandemic scenario around the world are increased at a tremendous rate. Hence our work focuses on effective knowledge retrieval using question and answering approach. Our experimental results are found to give better results based on Percentage closeness, precision, and recall parameters. KRQA has the novelty of retrieving more relevant answers with good quality. © 2022 IEEE.

A Question-Answering System on COVID-19 Scientific Literature

The vast amount of COVID-19 research literature has made it difficult for medical experts, clinical scientists, and researchers to keep up with the latest research findings. We present two datasets for COVID-19 in this work: (1) first, we create a dataset from the up-to-date scientific publications on COVID-19, and (2) second, we build a gold-standard dataset of question-answering pairs annotated by volunteer biomedical experts on COVID-19 related scientific articles. We develop a question-answering (QA) pipeline that uses the first dataset to provide answers related to COVID-19 questions;we fine-tune MPNet (a Transformer model) on our gold-standard dataset and use it in the QA pipeline to enhance its reading capability. We also use this gold-standard dataset to evaluate the QA pipeline. The proposed MPNet version on the gold-standard dataset outperformed previous datasets and models, achieving an Exact Match/Fl score of 69.72/78.50 %, respectively © 2022 IEEE.

CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice.

BACKGROUND: Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time. METHODS: This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work: a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics. RESULTS: Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%. CONCLUSION: CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions.

The COVID-19 Question Answering System Based on Knowledge Graph

The COVID-19 that emerged at the end of 2019 is the biggest public health emergency encountered by human in the past 100 years. In the face of COVID-19, people need to get correct, comprehensive and clear information. However, traditional information retrieval methods only return a collection of related web pages, and users need to distinguish the authenticity from redundant and complicated information. Therefore, such information acquisition methods are inefficient and cannot serve users well. To meet the needs of users for related information, it is necessary to study the question answering system for the COVID-19. This paper studies and builds a COVID-19 question answering system based on knowledge graph. In the System, the question answering function is realized by template matching, which based on the Naive Bayes algorithm. For the input questions, the system firstly performs entity recognition, using entity type labeling combined with entity similarity matching to identify entities in the user's questions. Then the system predicts the user's question intention and use the trained question classifier to predict the category number. Finally Cypher is utilized to query graph database to generate and output the answer. The system implemented in this paper can help users quickly obtain the information they want and improve the user's information acquisition efficiency. The system can provide people convenient and fast ways of obtaining information about COVID-19, such as medical treatment, health, materials, prevention and control, scientific research, so as to help people take precautions against diseases and decrease the incidence of COVID-19. © 2021 IEEE.

Enabling a Question-Answering System for COVID Using a Hybrid Approach Based on Wikipedia and Q/A Pairs

The research on COVID-19 disease has produced much information, but there are more questions than certainty. This proposal aims to contribute by providing reliable and updated answers to questions aimed at the general public. To achieve this goal, we design a question-answering architecture that leverages two information sources of different nature, controlled-official and open-collaborative. Thus, the system can answer several questions that the community may have about COVID. During the experimentation, we found that thanks to knowledge graphs, information retrieval, and NLP methods, the system can provide explainable answers;i.e., they obtain direct answers and can browse into enriched responses. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.