A Comparative Analysis of Modern Machine Learning Approaches for Automatic Classification of Scientific Articles

Automatic classification of scientific articles is very beneficial for the scientific research community to know whether the journal is appropriate or not. Specifically, it helps editor(s) pre-screen them at the editor’s desk. In such a scenario, modern machine learning approaches can help automatically classify scientific articles based on their abstracts. In this work, we classify scientific articles based on their category, and hence a comparative analysis work is performed where several deep learning and machine learning-based approaches are analyzed. Our experimental results suggest that the domain-specific pre-trained model SciBert helps in improving the classification performance significantly.