Abstract
Since the beginning of the COVID-19 pandemic almost two years ago, there have been more than 700,000 scientific papers published on the subject. An individual researcher cannot possibly get acquainted with such a huge text corpus and, therefore, some help from artificial intelligence (AI) is highly needed. We propose the AI-based tool to help researchers navigate the medical papers collections in a meaningful way and extract some knowledge from scientific COVID-19 papers. The main idea of our approach is to get as much semi-structured information from text corpus as possible, using named entity recognition (NER) with a model called PubMedBERT and Text Analytics for Health service, then store the data into NoSQL database for further fast processing and insights generation. Additionally, the contexts in which the entities were used (neutral or negative) are determined. Application of NLP and text-based emotion detection (TBED) methods to COVID-19 text corpus allows us to gain insights on important issues of diagnosis and treatment (such as changes in medical treatment over time, joint treatment strategies using several medications, and the connection between signs and symptoms of coronavirus, etc.).
Highlights
Automatic scientific paper analysis is a fast-growing area of study
Since the beginning of COVID-19 pandemic almost two years ago, there have been more than 700,000 scientific papers published on the subject
At the very beginning of COVID-19 pandemic, a research challenge has been launched on Kaggle to analyze scientific papers on the subject
Summary
Automatic scientific paper analysis is a fast-growing area of study. In recent years, there has been huge progress in the field of natural language processing (NLP), and very powerful neural network language models have been trained. At the very beginning of COVID-19 pandemic, a research challenge has been launched on Kaggle to analyze scientific papers on the subject The dataset behind this competition is called CORD, and it contains a constantly updated corpus of everything that is published on topics related to COVID-19 [7]. Resources for text mining researchers and practitioners include pretrained COVID-19 domain language models, knowledge graphs, and embeddings [12]. The proposed architecture uses NoSQL database to store oefn tity-relation metadata, which allows us to use DBMS SQL-based querying to perform semantically rich queries over text corpus. Another novelty of the proposed system is to aopvpelrytetexxttc-obrapseuds. Another novelty of the proposed system is to aopvpelrytetexxttc-obrapseuds. eAmnoottihoenr dneotveeclttiyonof(TthBeEpDr)opanodsekdnsoywstleemdgies tgoraapphpslytotesxht-obwascehdaenmgeostioinn mdeetdeicctaiol ntre(TatBmEeDn)taonvderktniomwelaedndgejogirnatpthresattomsehnotwstcrhataenggieess uinsimngedsiecvaelrtarleamtmedeincat toiovners.time and joint treatment strategies using several medications
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