An Information Retrieval and Extraction Tool for Covid-19 Related Papers

• URL: http://arxiv.org/abs/2401.16430v1
• Date: Sat, 20 Jan 2024 01:34:50 GMT
• Title: An Information Retrieval and Extraction Tool for Covid-19 Related Papers
• Authors: Marcos V. L. Pivetta
• Abstract summary: The main focus of this paper is to provide researchers with a better search tool for COVID-19 related papers. Our tool has shown the potential to assist researchers by automating a topic-based search of CORD-19 papers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorporates, in a novel way, aspects of Information Retrieval (IR) and Extraction (IE) applied to the COVID-19 Open Research Dataset (CORD-19). The main focus of this paper is to provide researchers with a better search tool for COVID-19 related papers, helping them find reference papers and hightlight relevant entities in text. Method: We applied Latent Dirichlet Allocation (LDA) to model, based on research aspects, the topics of all English abstracts in CORD-19. Relevant named entities of each abstract were extracted and linked to the corresponding UMLS concept. Regular expressions and the K-Nearest Neighbors algorithm were used to rank relevant papers. Results: Our tool has shown the potential to assist researchers by automating a topic-based search of CORD-19 papers. Nonetheless, we identified that more fine-tuned topic modeling parameters and increased accuracy of the research aspect classifier model could lead to a more accurate and reliable tool. Conclusion: We emphasize the need of new automated tools to help researchers find relevant COVID-19 documents, in addition to automatically extracting useful information contained in them. Our work suggests that combining different algorithms and models could lead to new ways of browsing COVID-19 paper data.

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