Biomedical
Semantic Pattern Detection in COVID-19 Using Contextual Clustering and Intelligent Topic Modeling
rnfinity
Pooja Kherwa,
Poonam Bansal
Abstract
The COVID-19 pandemic is the deadliest outbreak in our living memory. So, it is the need of hour to prepare the world with strategies to prevent and control the impact of the pandemic. In this paper, a novel semantic pattern detection approach in the COVID-19 literature using contextual clustering and intelligent topic modeling is presented. For contextual clustering, three level weights at term level, document level, and corpus level are used with latent semantic analysis. For intelligent topic modeling, semantic collocations using pointwise mutual information (PMI), and log frequency biased mutual dependency (LBMD) are selected, and latent dirichlet allocation is applied. Contextual clustering with latent semantic analysis presents semantic spaces with high correlation in terms at corpus level. Through intelligent topic modeling, topics are improved in the form of lower perplexity and highly coherent. This research helps in finding the knowledge gap in the area of COVID-19 research and offered direction for future research.
Key Questions
What is the main focus of this study?
The study introduces a novel approach to detect semantic patterns in COVID-19 literature by employing contextual clustering and intelligent topic modeling techniques.
What methodologies are utilized in the research?
The research employs contextual clustering with three-level weights at the term, document, and corpus levels, combined with latent semantic analysis. Additionally, intelligent topic modeling techniques are applied to identify semantic patterns in COVID-19 literature.
Why is this research significant?
Given the unprecedented impact of the COVID-19 pandemic, understanding semantic patterns in related literature can aid in developing strategies to prevent and control future epidemics. The proposed approach offers a systematic method to analyze vast amounts of COVID-19 research, potentially uncovering valuable insights.
What are the potential applications of this study?
The methodologies proposed can be applied to large datasets of scientific literature to detect underlying semantic patterns. This can assist researchers and policymakers in identifying key themes, emerging trends, and critical areas that require attention in the context of COVID-19 and other infectious diseases.
What are the limitations of the study?
While the study presents a novel approach, its effectiveness depends on the quality and comprehensiveness of the available literature. Additionally, the methodologies may require adaptation when applied to different datasets or in the context of other diseases.
