An ensemble approach for research article classification: a case study in artificial intelligence.

Text classification of research articles in emerging fields poses significant challenges due to their complex boundaries, interdisciplinary nature, and rapid evolution. Traditional methods, which rely on manually curated search terms and keyword matching, often lack recall due to the inherent incompleteness of keyword lists. In response to this limitation, this study introduces a deep learning-based ensemble approach that addresses the challenges of article classification in dynamic research areas, using the field of artificial intelligence (AI) as a case study. Our approach included using decision tree, sciBERT and regular expression matching on different fields of the articles, and a support vector machine (SVM) to merge the results from different models. We evaluated the effectiveness of our method on a manually labeled dataset, finding that our combined approach captured around 97% of AI-related articles in the web of science (WoS) corpus with a precision of 0.92. This presents a 0.15 increase in F1-score compared with existing search term based approach. Following this, we performed an ablation study to prove that each component in the ensemble model contributes to the overall performance, and that sciBERT outperforms other pre-trained BERT models in this case.