Target specific mining of COVID-19 scholarly articles using one-class approach

Abstract

In recent years, several research articles have been published in the field of corona-virus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of corona-virus related research articles using machine learning approaches. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.

References in corpus (9)

• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Distributed Representations of Sentences and Documents
• Deep Learning for Anomaly Detection: A Survey
• One-Class Classification: Taxonomy of Study and Review of Techniques
• Modern hierarchical, agglomerative clustering algorithms
• Document Embedding with Paragraph Vectors
• Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision

Cited by in corpus (4)

• Face Mask Detection using Transfer Learning of InceptionV3
• Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
• DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
• Repurposing TREC-COVID Annotations to Answer the Key Questions of CORD-19