Beyond original Research Articles Categorization via NLP
This work addresses the need for better navigation and recommendation in scientific research literature, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of categorizing scientific literature into unknown categories using NLP, achieving improved text categorization over the traditional arXiv labeling system.
This work proposes a novel approach to text categorization -- for unknown categories -- in the context of scientific literature, using Natural Language Processing techniques. The study leverages the power of pre-trained language models, specifically SciBERT, to extract meaningful representations of abstracts from the ArXiv dataset. Text categorization is performed using the K-Means algorithm, and the optimal number of clusters is determined based on the Silhouette score. The results demonstrate that the proposed approach captures subject information more effectively than the traditional arXiv labeling system, leading to improved text categorization. The approach offers potential for better navigation and recommendation systems in the rapidly growing landscape of scientific research literature.